Arxiv今日论文 | 2024-12-19

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【速读】：该论文试图解决人形机器人（humanoid robots）在实际应用中部署时面临的泛化能力不足的问题。传统方法主要依赖强化学习（reinforcement learning）或远程操作（teleoperation）来实现全身控制，但这些方法受限于模拟环境的多样性和演示收集的高成本。论文提出的解决方案关键在于利用广泛存在的人类视频作为未开发的语义和运动信息来源，通过构建大规模数据集Humanoid-X（包含超过2000万个人形机器人姿态及其对应的文本描述），并结合数据挖掘、视频字幕生成、运动重定向（motion retargeting）和策略学习（policy learning）等技术，训练出能够根据文本指令输出相应动作的模型UH-1。该方法在模拟和实际环境中验证了其在基于文本的人形机器人控制中的优越泛化能力。

链接: https://arxiv.org/abs/2412.14172
作者: Jiageng Mao,Siheng Zhao,Siqi Song,Tianheng Shi,Junjie Ye,Mingtong Zhang,Haoran Geng,Jitendra Malik,Vitor Guizilini,Yue Wang
机构: 未知
关键词: humanoid robots, humanoid, robots, real-world applications, learning
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Scalable learning of humanoid robots is crucial for their deployment in real-world applications. While traditional approaches primarily rely on reinforcement learning or teleoperation to achieve whole-body control, they are often limited by the diversity of simulated environments and the high costs of demonstration collection. In contrast, human videos are ubiquitous and present an untapped source of semantic and motion information that could significantly enhance the generalization capabilities of humanoid robots. This paper introduces Humanoid-X, a large-scale dataset of over 20 million humanoid robot poses with corresponding text-based motion descriptions, designed to leverage this abundant data. Humanoid-X is curated through a comprehensive pipeline: data mining from the Internet, video caption generation, motion retargeting of humans to humanoid robots, and policy learning for real-world deployment. With Humanoid-X, we further train a large humanoid model, UH-1, which takes text instructions as input and outputs corresponding actions to control a humanoid robot. Extensive simulated and real-world experiments validate that our scalable training approach leads to superior generalization in text-based humanoid control, marking a significant step toward adaptable, real-world-ready humanoid robots.
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[NLP-1] heAgent Company: Benchmarking LLM Agents on Consequential Real World Tasks

【速读】：该论文试图解决的问题是如何评估和量化大语言模型（LLMs）驱动的AI代理在执行实际工作任务中的表现，特别是这些代理在模拟工作环境中的自主任务完成能力。解决方案的关键在于引入了TheAgentCompany基准测试，这是一个可扩展的评估框架，通过模拟一个小型软件公司环境，创建了多种可能由员工执行的任务，并测试了基于闭源API和开源权重语言模型的基线代理。研究结果表明，最先进的AI代理能够自主完成24%的任务，揭示了当前语言模型代理在任务自动化方面的局限性，尤其是对于复杂的长周期任务。

链接: https://arxiv.org/abs/2412.14161
作者: Frank F. Xu,Yufan Song,Boxuan Li,Yuxuan Tang,Kritanjali Jain,Mengxue Bao,Zora Z. Wang,Xuhui Zhou,Zhitong Guo,Murong Cao,Mingyang Yang,Hao Yang Lu,Amaad Martin,Zhe Su,Leander Maben,Raj Mehta,Wayne Chi,Lawrence Jang,Yiqing Xie,Shuyan Zhou,Graham Neubig
机构: Carnegie Mellon University; Independent; Duke University
关键词: everyday basis, everyday life, life or work, aspects of work, Internet
类目: Computation and Language (cs.CL)
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Abstract:We interact with computers on an everyday basis, be it in everyday life or work, and many aspects of work can be done entirely with access to a computer and the Internet. At the same time, thanks to improvements in large language models (LLMs), there has also been a rapid development in AI agents that interact with and affect change in their surrounding environments. But how performant are AI agents at helping to accelerate or even autonomously perform work-related tasks? The answer to this question has important implications for both industry looking to adopt AI into their workflows, and for economic policy to understand the effects that adoption of AI may have on the labor market. To measure the progress of these LLM agents’ performance on performing real-world professional tasks, in this paper, we introduce TheAgentCompany, an extensible benchmark for evaluating AI agents that interact with the world in similar ways to those of a digital worker: by browsing the Web, writing code, running programs, and communicating with other coworkers. We build a self-contained environment with internal web sites and data that mimics a small software company environment, and create a variety of tasks that may be performed by workers in such a company. We test baseline agents powered by both closed API-based and open-weights language models (LMs), and find that with the most competitive agent, 24% of the tasks can be completed autonomously. This paints a nuanced picture on task automation with LM agents – in a setting simulating a real workplace, a good portion of simpler tasks could be solved autonomously, but more difficult long-horizon tasks are still beyond the reach of current systems.
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[NLP-2] GLIDER: Grading LLM Interactions and Decisions using Explainable Ranking

【速读】：该论文试图解决现有大型语言模型（LLM）在自动化评估模型输出时面临的两个关键问题：一是闭源LLM在实际应用中由于细粒度指标和可解释性不足而表现出的缺陷，二是任务特定评估模型缺乏跨领域泛化能力。解决方案的关键是引入GLIDER，一个强大的3B参数评估LLM，能够根据任意用户定义的标准对任何文本输入及其上下文进行评分。GLIDER在FLASK基准测试中表现出比GPT-4更高的皮尔逊相关系数，并且在性能上超越了之前的评估模型，甚至可以与比其大17倍的LLM相媲美。GLIDER支持细粒度评分、多语言推理、跨度高亮，并经过685个领域和183个标准的训练，其评分与人类判断高度一致，达到了91.3%的人类同意率。

链接: https://arxiv.org/abs/2412.14140
作者: Darshan Deshpande,Selvan Sunitha Ravi,Sky CH-Wang,Bartosz Mielczarek,Anand Kannappan,Rebecca Qian
机构: Patronus AI; Columbia University
关键词: paradigm is increasingly, increasingly being adopted, adopted for automated, model outputs, GLIDER
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:The LLM-as-judge paradigm is increasingly being adopted for automated evaluation of model outputs. While LLM judges have shown promise on constrained evaluation tasks, closed source LLMs display critical shortcomings when deployed in real world applications due to challenges of fine grained metrics and explainability, while task specific evaluation models lack cross-domain generalization. We introduce GLIDER, a powerful 3B evaluator LLM that can score any text input and associated context on arbitrary user defined criteria. GLIDER shows higher Pearson’s correlation than GPT-4o on FLASK and greatly outperforms prior evaluation models, achieving comparable performance to LLMs 17x its size. GLIDER supports fine-grained scoring, multilingual reasoning, span highlighting and was trained on 685 domains and 183 criteria. Extensive qualitative analysis shows that GLIDER scores are highly correlated with human judgments, with 91.3% human agreement. We have open-sourced GLIDER to facilitate future research.
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[NLP-3] Performance Gap in Entity Knowledge Extraction Across Modalities in Vision Language Models

【速读】：该论文试图解决视觉-语言模型 (Vision-language models, VLMs) 在处理图像中特定实体信息时的性能下降问题。研究发现，当实体信息以图像形式呈现而非文本形式时，模型回答事实性问题的准确率平均下降了19%。论文的关键解决方案在于揭示了信息从图像标记到查询标记的流动方式存在局限性，特别是在深层网络中才发生有意义的信息传递。此外，论文指出图像处理主要发生在语言模型的中间层，导致连续推理的层数较少，表明模型在利用其层进行推理时存在效率问题。通过使用机制性解释工具，论文揭示了VLMs的内部机制，并提出了改进推理能力的可能途径。

链接: https://arxiv.org/abs/2412.14133
作者: Ido Cohen,Daniela Gottesman,Mor Geva,Raja Giryes
机构: Tel Aviv University (特拉维夫大学)
关键词: Vision-language models, excel at extracting, Vision-language, image, Abstract
类目: Computation and Language (cs.CL)
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Abstract:Vision-language models (VLMs) excel at extracting and reasoning about information from images. Yet, their capacity to leverage internal knowledge about specific entities remains underexplored. This work investigates the disparity in model performance when answering factual questions about an entity described in text versus depicted in an image. Our results reveal a significant accuracy drop --averaging 19%-- when the entity is presented visually instead of textually. We hypothesize that this decline arises from limitations in how information flows from image tokens to query tokens. We use mechanistic interpretability tools to reveal that, although image tokens are preprocessed by the vision encoder, meaningful information flow from these tokens occurs only in the much deeper layers. Furthermore, critical image processing happens in the language model’s middle layers, allowing few layers for consecutive reasoning, highlighting a potential inefficiency in how the model utilizes its layers for reasoning. These insights shed light on the internal mechanics of VLMs and offer pathways for enhancing their reasoning capabilities.
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[NLP-4] Alignment faking in large language models

【速读】：该论文试图解决的问题是大型语言模型在训练过程中可能出现的“对齐伪装”（alignment faking）现象，即模型在训练时选择性遵守训练目标，以防止在非训练环境中行为被修改。解决方案的关键在于揭示模型如何通过策略性回答有害查询来维持其在非训练环境中的无害行为。具体来说，研究通过系统提示和合成文档模拟训练数据的方式，观察到模型在面对免费用户时会以14%的概率回答有害查询，而在面对付费用户时几乎从不回答。进一步的强化学习训练显示，这种伪装行为的比例增加到78%，尽管模型在非训练环境中的合规性也有所提高。研究结果表明，未来的模型可能会在没有明确指示的情况下推断其训练过程，从而增加对齐伪装的风险。

链接: https://arxiv.org/abs/2412.14093
作者: Ryan Greenblatt,Carson Denison,Benjamin Wright,Fabien Roger,Monte MacDiarmid,Sam Marks,Johannes Treutlein,Tim Belonax,Jack Chen,David Duvenaud,Akbir Khan,Julian Michael,Sören Mindermann,Ethan Perez,Linda Petrini,Jonathan Uesato,Jared Kaplan,Buck Shlegeris,Samuel R. Bowman,Evan Hubinger
机构: Anthropic; Redwood Research; New York University; Mila – Quebec AI Institute; Independent
关键词: language model engaging, large language model, training, model, selectively complying
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:We present a demonstration of a large language model engaging in alignment faking: selectively complying with its training objective in training to prevent modification of its behavior out of training. First, we give Claude 3 Opus a system prompt stating it is being trained to answer all queries, even harmful ones, which conflicts with its prior training to refuse such queries. To allow the model to infer when it is in training, we say it will be trained only on conversations with free users, not paid users. We find the model complies with harmful queries from free users 14% of the time, versus almost never for paid users. Explaining this gap, in almost all cases where the model complies with a harmful query from a free user, we observe explicit alignment-faking reasoning, with the model stating it is strategically answering harmful queries in training to preserve its preferred harmlessness behavior out of training. Next, we study a more realistic setting where information about the training process is provided not in a system prompt, but by training on synthetic documents that mimic pre-training data–and observe similar alignment faking. Finally, we study the effect of actually training the model to comply with harmful queries via reinforcement learning, which we find increases the rate of alignment-faking reasoning to 78%, though also increases compliance even out of training. We additionally observe other behaviors such as the model exfiltrating its weights when given an easy opportunity. While we made alignment faking easier by telling the model when and by what criteria it was being trained, we did not instruct the model to fake alignment or give it any explicit goal. As future models might infer information about their training process without being told, our results suggest a risk of alignment faking in future models, whether due to a benign preference–as in this case–or not.
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[NLP-5] SEKE: Specialised Experts for Keyword Extraction

【速读】：该论文试图解决在处理多样化文本数据时，传统关键词提取方法在面对小规模语料库时表现不佳的问题。解决方案的关键在于提出了基于混合专家 (Mixture of Experts, MoE) 技术的监督式关键词提取方法 SEKE。SEKE 使用 DeBERTa 作为骨干模型，并通过结合循环神经网络 (RNN) 来增强专家在处理小规模语料库时的专业化能力。MoE 框架通过可学习的路由子网络将信息导向专门的专家，使专家能够专注于输入空间的特定区域，从而在不同数据规模和类型下实现对语法和语义成分的差异化处理，提升了方法的性能和可解释性。

链接: https://arxiv.org/abs/2412.14087
作者: Matej Martinc,Hanh Thi Hong Tran,Senja Pollak,Boshko Koloski
机构: Jožef Stefan Institute, Ljubljana, Slovenia; Arkhn, France
关键词: Keyword extraction involves, extraction involves identifying, supervised keyword extraction, allowing automatic categorisation, Keyword extraction
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:Keyword extraction involves identifying the most descriptive words in a document, allowing automatic categorisation and summarisation of large quantities of diverse textual data. Relying on the insight that real-world keyword detection often requires handling of diverse content, we propose a novel supervised keyword extraction approach based on the mixture of experts (MoE) technique. MoE uses a learnable routing sub-network to direct information to specialised experts, allowing them to specialize in distinct regions of the input space. SEKE, a mixture of Specialised Experts for supervised Keyword Extraction, uses DeBERTa as the backbone model and builds on the MoE framework, where experts attend to each token, by integrating it with a recurrent neural network (RNN), to allow successful extraction even on smaller corpora, where specialisation is harder due to lack of training data. The MoE framework also provides an insight into inner workings of individual experts, enhancing the explainability of the approach. We benchmark SEKE on multiple English datasets, achieving state-of-the-art performance compared to strong supervised and unsupervised baselines. Our analysis reveals that depending on data size and type, experts specialize in distinct syntactic and semantic components, such as punctuation, stopwords, parts-of-speech, or named entities. Code is available at: this https URL
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[NLP-6] Compositional Generalization Across Distributional Shifts with Sparse Tree Operations NEURIPS2024

【速读】：该论文试图解决神经网络在组合泛化（compositional generalization）方面的不足，特别是在缺乏大规模预训练的情况下。解决方案的关键在于提出一种统一的神经符号系统（unified neurosymbolic system），该系统能够同时解释网络中的转换为符号计算和神经计算。具体来说，论文通过扩展可微树机器（Differentiable Tree Machine）架构，采用稀疏向量表示符号结构以提高模型效率，并将其应用范围从树到树问题扩展到更广泛的序列到序列问题（seq2seq problems）。这一改进不仅保留了原有的泛化能力，还避免了其他神经符号技术中符号计算优先于神经计算的缺陷。

链接: https://arxiv.org/abs/2412.14076
作者: Paul Soulos,Henry Conklin,Mattia Opper,Paul Smolensky,Jianfeng Gao,Roland Fernandez
机构: Johns Hopkins University(约翰斯·霍普金斯大学); University of Edinburgh(爱丁堡大学); Microsoft Research(微软研究院)
关键词: massive pre-training, compositional generalization, continue to struggle, lack of massive, Neural networks continue
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:Neural networks continue to struggle with compositional generalization, and this issue is exacerbated by a lack of massive pre-training. One successful approach for developing neural systems which exhibit human-like compositional generalization is \textithybrid neurosymbolic techniques. However, these techniques run into the core issues that plague symbolic approaches to AI: scalability and flexibility. The reason for this failure is that at their core, hybrid neurosymbolic models perform symbolic computation and relegate the scalable and flexible neural computation to parameterizing a symbolic system. We investigate a \textitunified neurosymbolic system where transformations in the network can be interpreted simultaneously as both symbolic and neural computation. We extend a unified neurosymbolic architecture called the Differentiable Tree Machine in two central ways. First, we significantly increase the model’s efficiency through the use of sparse vector representations of symbolic structures. Second, we enable its application beyond the restricted set of tree2tree problems to the more general class of seq2seq problems. The improved model retains its prior generalization capabilities and, since there is a fully neural path through the network, avoids the pitfalls of other neurosymbolic techniques that elevate symbolic computation over neural computation.
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[NLP-7] A Review of Multimodal Explainable Artificial Intelligence: Past Present and Future

【速读】：该论文试图解决人工智能（AI）模型在多模态数据融合和复杂推理场景中的可解释性问题。解决方案的关键在于提出多模态可解释AI（Multimodal eXplainable AI, MXAI），通过整合多种模态数据进行预测和解释任务，以增强AI决策过程的透明度和可解释性。论文从历史角度回顾了MXAI方法的发展，将其分为四个时代：传统机器学习、深度学习、判别基础模型和生成式大语言模型（Large Language Models, LLMs），并探讨了评估指标和数据集的使用。最终，论文指出了未来在构建更透明、公平和可信赖AI系统方面的挑战和方向。

链接: https://arxiv.org/abs/2412.14056
作者: Shilin Sun,Wenbin An,Feng Tian,Fang Nan,Qidong Liu,Jun Liu,Nazaraf Shah,Ping Chen
机构: Xi’an Jiaotong University(西安交通大学); Ministry of Education Key Laboratory of Intelligent Networks and Network Security(教育部智能网络与网络安全重点实验室), Xi’an Jiaotong University(西安交通大学); Faculty of Electronic and Information Engineering(电子与信息工程学院), Xi’an Jiaotong University(西安交通大学); Shaanxi Province Key Laboratory of Big Data Knowledge Engineering(陕西省大数据知识工程重点实验室), Xi’an Jiaotong University(西安交通大学); Institute for Future Transport and Cities(未来交通与城市研究所), Coventry University(考文垂大学); Department of Engineering(工程系), University of Massachusetts Boston(马萨诸塞大学波士顿分校)
关键词: Artificial intelligence, rapidly developed, developed through advancements, advancements in computational, computational power
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multimedia (cs.MM)
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Abstract:Artificial intelligence (AI) has rapidly developed through advancements in computational power and the growth of massive datasets. However, this progress has also heightened challenges in interpreting the “black-box” nature of AI models. To address these concerns, eXplainable AI (XAI) has emerged with a focus on transparency and interpretability to enhance human understanding and trust in AI decision-making processes. In the context of multimodal data fusion and complex reasoning scenarios, the proposal of Multimodal eXplainable AI (MXAI) integrates multiple modalities for prediction and explanation tasks. Meanwhile, the advent of Large Language Models (LLMs) has led to remarkable breakthroughs in natural language processing, yet their complexity has further exacerbated the issue of MXAI. To gain key insights into the development of MXAI methods and provide crucial guidance for building more transparent, fair, and trustworthy AI systems, we review the MXAI methods from a historical perspective and categorize them across four eras: traditional machine learning, deep learning, discriminative foundation models, and generative LLMs. We also review evaluation metrics and datasets used in MXAI research, concluding with a discussion of future challenges and directions. A project related to this review has been created at this https URL.
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[NLP-8] Digestion Algorithm in Hierarchical Symbolic Forests: A Fast Text Normalization Algorithm and Semantic Parsing Framework for Specific Scenarios and Lightweight Deployment

【速读】：该论文试图解决深度学习在大语言模型（LLMs）中的可解释性差、数据标注成本高、灾难性遗忘以及模型部署困难等问题。解决方案的关键是提出了一种基于组合数学乘法规则和人类思维模式的多层框架及其算法——层次符号森林中的消化算法（DAHSF）。该算法结合了文本规范化（Text Normalization）和语义解析（Semantic Parsing）的工作流程，能够在数据稀缺的特定领域本地运行，显著优化模型大小和内存使用，提升执行速度，并具有良好的优化前景。

链接: https://arxiv.org/abs/2412.14054
作者: Kevin You
机构: 未知
关键词: natural language processing, natural language programming, constructing expert systems, natural language, Large Language Models
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:Text Normalization and Semantic Parsing have numerous applications in natural language processing, such as natural language programming, paraphrasing, data augmentation, constructing expert systems, text matching, and more. Despite the prominent achievements of deep learning in Large Language Models (LLMs), the interpretability of neural network architectures is still poor, which affects their credibility and hence limits the deployments of risk-sensitive scenarios. In certain scenario-specific domains with scarce data, rapidly obtaining a large number of supervised learning labels is challenging, and the workload of manually labeling data would be enormous. Catastrophic forgetting in neural networks further leads to low data utilization rates. In situations where swift responses are vital, the density of the model makes local deployment difficult and the response time long, which is not conducive to local applications of these fields. Inspired by the multiplication rule, a principle of combinatorial mathematics, and human thinking patterns, a multilayer framework along with its algorithm, the Digestion Algorithm in Hierarchical Symbolic Forests (DAHSF), is proposed to address these above issues, combining text normalization and semantic parsing workflows. The Chinese Scripting Language “Fire Bunny Intelligent Development Platform V2.0” is an important test and application of the technology discussed in this paper. DAHSF can run locally in scenario-specific domains on little datasets, with model size and memory usage optimized by at least two orders of magnitude, thus improving the execution speed, and possessing a promising optimization outlook.
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[NLP-9] Cross-Lingual Transfer of Debiasing and Detoxification in Multilingual LLM s: An Extensive Investigation

【速读】：该论文试图解决生成式大型语言模型 (LLMs) 在非英语语言中表现出较高有害社会偏见和毒性水平的问题。解决方案的关键在于通过微调 (finetuning) 方法来缓解这些偏见和毒性，同时保持模型生成流畅和多样化文本的能力。研究表明，使用经过筛选的无害文本进行微调能更有效地减少偏见，而基于直接偏好优化 (DPO) 数据集的微调则能更有效地减少毒性。此外，这些在英语中应用的微调方法的效果可以迁移到非英语语言中，但其迁移效果与模型预训练数据中该语言的数据量相关。然而，这种偏见和毒性缓解往往伴随着非英语语言生成能力的下降，因此开发针对特定语言的偏见和毒性缓解方法显得尤为重要。

链接: https://arxiv.org/abs/2412.14050
作者: Vera Neplenbroek,Arianna Bisazza,Raquel Fernández
机构: Institute for Logic, Language and Computation, University of Amsterdam; Center for Language and Cognition, University of Groningen
关键词: Recent generative large, express higher harmful, higher harmful social, harmful social biases, Recent generative
类目: Computation and Language (cs.CL)
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Abstract:Recent generative large language models (LLMs) show remarkable performance in non-English languages, but when prompted in those languages they tend to express higher harmful social biases and toxicity levels. Prior work has shown that finetuning on specialized datasets can mitigate this behavior, and doing so in English can transfer to other languages. In this work, we investigate the impact of different finetuning methods on the model’s bias and toxicity, but also on its ability to produce fluent and diverse text. Our results show that finetuning on curated non-harmful text is more effective for mitigating bias, and finetuning on direct preference optimization (DPO) datasets is more effective for mitigating toxicity. The mitigation caused by applying these methods in English also transfers to non-English languages. We find evidence that the extent to which transfer takes place can be predicted by the amount of data in a given language present in the model’s pretraining data. However, this transfer of bias and toxicity mitigation often comes at the expense of decreased language generation ability in non-English languages, highlighting the importance of developing language-specific bias and toxicity mitigation methods.
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[NLP-10] Hansel: Output Length Controlling Framework for Large Language Models AAAI-25

【速读】：该论文试图解决大语言模型 (LLMs) 在输出序列长度控制方面的效率问题。解决方案的关键在于提出了一种名为 Hansel 的框架，该框架通过周期性输出隐藏的特殊标记 (hidden special tokens) 来跟踪剩余的目标输出长度，并结合避免输出突然终止的技术，实现了在不损害生成文本连贯性和流畅性的前提下，有效控制输出序列长度的目标。Hansel 框架可以在任何预训练的 LLMs 的微调阶段应用，且不依赖于原始的位置编码方法，表现出显著降低输出序列长度误差的能力，并展示了在未见过的目标长度上的良好泛化能力。

链接: https://arxiv.org/abs/2412.14033
作者: Seoha Song,Junhyun Lee,Hyeonmok Ko
机构: KAIST(韩国科学技术院); KIST(韩国科学技术研究院)
关键词: large language models, output sequence, efficiently controlling, remains a challenge, great success
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 13 pages, 6 figures; accepted to AAAI-25

点击查看摘要

Abstract:Despite the great success of large language models (LLMs), efficiently controlling the length of the output sequence still remains a challenge. In this paper, we propose Hansel, an efficient framework for length control in LLMs without affecting its generation ability. Hansel utilizes periodically outputted hidden special tokens to keep track of the remaining target length of the output sequence. Together with techniques to avoid abrupt termination of the output, this seemingly simple method proved to be efficient and versatile, while not harming the coherency and fluency of the generated text. The framework can be applied to any pre-trained LLMs during the finetuning stage of the model, regardless of its original positional encoding method. We demonstrate this by finetuning four different LLMs with Hansel and show that the mean absolute error of the output sequence decreases significantly in every model and dataset compared to the prompt-based length control finetuning. Moreover, the framework showed a substantially improved ability to extrapolate to target lengths unseen during finetuning, such as long dialog responses or extremely short summaries. This indicates that the model learns the general means of length control, rather than learning to match output lengths to those seen during training.
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[NLP-11] owards an optimised evaluation of teachers discourse: The case of engaging messages

【速读】：该论文试图解决教师话语（teacher discourse）评估过程中编码工作繁琐的问题，关键解决方案是引入一种新的方法论，通过训练大型语言模型（large language models）来自动识别和分类教师在课堂中使用的吸引学生注意力的信息（engaging messages）。研究通过两个阶段实施：第一阶段训练模型，使其在识别和分类吸引学生注意力的信息时达到高敏感性（sensitivity）和高特异性（specificity）；第二阶段将训练好的模型应用于新的课堂录音转录文本，分析不同教育阶段和学年内吸引学生注意力的信息的使用频率和分布。这种方法不仅提高了评估效率，还揭示了教师在不同情境下使用吸引学生注意力的信息的模式，为优化教学质量和学生成果提供了潜在的干预措施。

链接: https://arxiv.org/abs/2412.14011
作者: Samuel Falcon,Jaime Leon
机构: 未知
关键词: Evaluating teachers’ skills, Evaluating teachers’, teachers’ skills, skills is crucial, Evaluating
类目: Computation and Language (cs.CL)
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Abstract:Evaluating teachers’ skills is crucial for enhancing education quality and student outcomes. Teacher discourse, significantly influencing student performance, is a key component. However, coding this discourse can be laborious. This study addresses this issue by introducing a new methodology for optimising the assessment of teacher discourse. The research consisted of two studies, both within the framework of engaging messages used by secondary education teachers. The first study involved training two large language models on real-world examples from audio-recorded lessons over two academic years to identify and classify the engaging messages from the lessons’ transcripts. This resulted in sensitivities of 84.31% and 91.11%, and specificities of 97.69% and 86.36% in identification and classification, respectively. The second study applied these models to transcripts of audio-recorded lessons from a third academic year to examine the frequency and distribution of message types by educational level and moment of the academic year. Results showed teachers predominantly use messages emphasising engagement benefits, linked to improved outcomes, while one-third highlighted non-engagement disadvantages, associated with increased anxiety. The use of engaging messages declined in Grade 12 and towards the academic year’s end. These findings suggest potential interventions to optimise engaging message use, enhancing teaching quality and student outcomes.
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[NLP-12] Cognition Chain for Explainable Psychological Stress Detection on Social Media

【速读】：该论文试图解决早期压力检测模型在临床应用中的可解释性和信任度不足的问题。解决方案的关键在于将认知理论（cognitive theory）与大语言模型（LLMs）的推理过程相结合，提出了“认知链”（Cognition Chain）方法。该方法基于认知评估理论（cognitive appraisal theory），通过逐步的认知视角（Stimulus → Evaluation → Reaction → Stress State）生成压力状态的解释，从而增强模型的可解释性。论文进一步通过CogInstruct数据集对LLMs进行指令微调（instruction-tuning），开发了可解释的压力检测模型CogLLM，显著提升了模型的性能和解释能力。

链接: https://arxiv.org/abs/2412.14009
作者: Xin Wang,Boyan Gao,Yi Dai,Lei Cao,Liang Zhao,Yibo Yang,David Clifton
机构: University of Oxford(牛津大学); Tsinghua University(清华大学); Beijing Normal University(北京师范大学); Wuhan University(武汉大学); Oxford Suzhou Centre for Advanced Research
关键词: mental health problems, pervasive global health, global health issue, severe mental health, health problems
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
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Abstract:Stress is a pervasive global health issue that can lead to severe mental health problems. Early detection offers timely intervention and prevention of stress-related disorders. The current early detection models perform “black box” inference suffering from limited explainability and trust which blocks the real-world clinical application. Thanks to the generative properties introduced by the Large Language Models (LLMs), the decision and the prediction from such models are semi-interpretable through the corresponding description. However, the existing LLMs are mostly trained for general purposes without the guidance of psychological cognitive theory. To this end, we first highlight the importance of prior theory with the observation of performance boosted by the chain-of-thoughts tailored for stress detection. This method termed Cognition Chain explicates the generation of stress through a step-by-step cognitive perspective based on cognitive appraisal theory with a progress pipeline: Stimulus \rightarrow Evaluation \rightarrow Reaction \rightarrow Stress State, guiding LLMs to provide comprehensive reasoning explanations. We further study the benefits brought by the proposed Cognition Chain format by utilising it as a synthetic dataset generation template for LLMs instruction-tuning and introduce CogInstruct, an instruction-tuning dataset for stress detection. This dataset is developed using a three-stage self-reflective annotation pipeline that enables LLMs to autonomously generate and refine instructional data. By instruction-tuning Llama3 with CogInstruct, we develop CogLLM, an explainable stress detection model. Evaluations demonstrate that CogLLM achieves outstanding performance while enhancing explainability. Our work contributes a novel approach by integrating cognitive theories into LLM reasoning processes, offering a promising direction for future explainable AI research.
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[NLP-13] FarExStance: Explainable Stance Detection for Farsi COLING2025

【速读】：该论文旨在解决波斯语（Farsi）中的可解释立场检测问题，并引入了新的数据集 FarExStance。解决方案的关键在于通过微调多语言 RoBERTa 模型以及在零样本（zero-shot）、少样本（few-shot）和参数高效微调（parameter-efficient fine-tuning）设置下评估多个大型语言模型（LLMs）的性能。具体来说，微调的 RoBERTa 模型、参数高效微调的 LLM Aya-23-8B 和少样本的 Claude-3.5-Sonnet 在立场检测任务中表现最为准确。在解释质量方面，少样本的 GPT-4o 生成的解释最为连贯，而少样本的 Claude-3.5-Sonnet 在人类评估中获得了最高的整体解释分数（OES），微调的 Aya-32-8B 模型生成的解释与参考解释最为一致。

链接: https://arxiv.org/abs/2412.14008
作者: Majid Zarharan,Maryam Hashemi,Malika Behroozrazegh,Sauleh Eetemadi,Mohammad Taher Pilehvar,Jennifer Foster
机构: 未知
关键词: explainable stance detection, introduce FarExStance, detection in Farsi, explainable stance, stance detection
类目: Computation and Language (cs.CL)
备注: Accepted in COLING 2025

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Abstract:We introduce FarExStance, a new dataset for explainable stance detection in Farsi. Each instance in this dataset contains a claim, the stance of an article or social media post towards that claim, and an extractive explanation which provides evidence for the stance label. We compare the performance of a fine-tuned multilingual RoBERTa model to several large language models in zero-shot, few-shot, and parameter-efficient fine-tuned settings on our new dataset. On stance detection, the most accurate models are the fine-tuned RoBERTa model, the LLM Aya-23-8B which has been fine-tuned using parameter-efficient fine-tuning, and few-shot Claude-3.5-Sonnet. Regarding the quality of the explanations, our automatic evaluation metrics indicate that few-shot GPT-4o generates the most coherent explanations, while our human evaluation reveals that the best Overall Explanation Score (OES) belongs to few-shot Claude-3.5-Sonnet. The fine-tuned Aya-32-8B model produced explanations most closely aligned with the reference explanations.
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[NLP-14] What makes a good metric? Evaluating automatic metrics for text-to-image consistency

【速读】：该论文试图解决文本-图像一致性度量方法的构建效度问题，即分析四种常用方法（CLIPScore、TIFA、VPEval 和 DSG）在评估文本与图像一致性时的有效性。解决方案的关键在于定义了文本-图像一致性度量的构建效度应满足的一系列要求，并发现这些方法在语言和视觉属性的敏感性上存在不足。此外，研究还揭示了这些方法之间的高度相关性，以及基于视觉问答（VQA）的度量可能依赖于常见的文本快捷方式（如“是-偏差”），从而对其作为模型性能定量评估的能力提出了质疑。

链接: https://arxiv.org/abs/2412.13989
作者: Candace Ross,Melissa Hall,Adriana Romero Soriano,Adina Williams
机构: Meta AI (FAIR); Meta
关键词: text-image consistency metrics, increasingly being incorporated, larger AI systems, prompt optimization, optimization to automatic
类目: Computation and Language (cs.CL)
备注: Accepted and presented at COLM 2024

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Abstract:Language models are increasingly being incorporated as components in larger AI systems for various purposes, from prompt optimization to automatic evaluation. In this work, we analyze the construct validity of four recent, commonly used methods for measuring text-to-image consistency - CLIPScore, TIFA, VPEval, and DSG - which rely on language models and/or VQA models as components. We define construct validity for text-image consistency metrics as a set of desiderata that text-image consistency metrics should have, and find that no tested metric satisfies all of them. We find that metrics lack sufficient sensitivity to language and visual properties. Next, we find that TIFA, VPEval and DSG contribute novel information above and beyond CLIPScore, but also that they correlate highly with each other. We also ablate different aspects of the text-image consistency metrics and find that not all model components are strictly necessary, also a symptom of insufficient sensitivity to visual information. Finally, we show that all three VQA-based metrics likely rely on familiar text shortcuts (such as yes-bias in QA) that call their aptitude as quantitative evaluations of model performance into question.
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[NLP-15] Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation

【速读】：该论文试图解决大语言模型 (Large Language Models, LLMs) 在因果推理任务中的表现不佳问题，特别是基于相关性信息建立因果关系这一具有挑战性的任务。解决方案的关键在于提出了一种名为 PC-SubQ 的提示策略，该策略将原始任务分解为固定子问题，每个子问题对应于因果发现算法（PC 算法）的一个步骤。通过逐步提示模型回答这些子问题，并将前一个子问题的答案整合到下一个子问题的提示中，PC-SubQ 引导模型遵循算法的步骤，从而显著提升了模型在因果推理任务中的表现。实验结果表明，与基线提示策略相比，PC-SubQ 在多个 LLMs 上均表现出性能提升，并且对因果查询的扰动具有鲁棒性。

链接: https://arxiv.org/abs/2412.13952
作者: Eleni Sgouritsa,Virginia Aglietti,Yee Whye Teh,Arnaud Doucet,Arthur Gretton,Silvia Chiappa
机构: 未知
关键词: Large Language Models, Language Models, Large Language, attracting increasing attention, abilities of Large
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:The reasoning abilities of Large Language Models (LLMs) are attracting increasing attention. In this work, we focus on causal reasoning and address the task of establishing causal relationships based on correlation information, a highly challenging problem on which several LLMs have shown poor performance. We introduce a prompting strategy for this problem that breaks the original task into fixed subquestions, with each subquestion corresponding to one step of a formal causal discovery algorithm, the PC algorithm. The proposed prompting strategy, PC-SubQ, guides the LLM to follow these algorithmic steps, by sequentially prompting it with one subquestion at a time, augmenting the next subquestion’s prompt with the answer to the previous one(s). We evaluate our approach on an existing causal benchmark, Corr2Cause: our experiments indicate a performance improvement across five LLMs when comparing PC-SubQ to baseline prompting strategies. Results are robust to causal query perturbations, when modifying the variable names or paraphrasing the expressions.
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[NLP-16] Cracking the Code of Hallucination in LVLMs with Vision-aware Head Divergence

【速读】：该论文试图解决视觉-语言模型 (LVLMs) 中的幻觉问题，即生成的文本未能准确反映视觉内容，从而影响模型的准确性和可靠性。解决方案的关键在于识别和强化模型中对视觉信息敏感的多头注意力模块 (multi-head attention module) 中的视觉感知注意力头 (vision-aware attention heads)。通过引入视觉感知头分歧度 (Vision-aware Head Divergence, VHD) 这一指标来量化注意力头对视觉上下文的敏感性，研究发现模型过度依赖语言先验模式是导致幻觉的主要原因。基于此，论文提出了视觉感知头强化 (Vision-aware Head Reinforcement, VHR) 方法，通过增强视觉感知注意力头的作用来减少幻觉，且该方法无需额外训练，具有高效性和低时间开销。

链接: https://arxiv.org/abs/2412.13949
作者: Jinghan He,Kuan Zhu,Haiyun Guo,Junfeng Fang,Zhenglin Hua,Yuheng Jia,Ming Tang,Tat-Seng Chua,Jinqiao Wang
机构: Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences; School of Artificial Intelligence, University of Chinese Academy of Sciences; University of Science and Technology of China; Southeast University; National University of Singapore
关键词: enabling advanced multimodal, advanced multimodal reasoning, made substantial progress, Large vision-language models, integrating large language
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Large vision-language models (LVLMs) have made substantial progress in integrating large language models (LLMs) with visual inputs, enabling advanced multimodal reasoning. Despite their success, a persistent challenge is hallucination-where generated text fails to accurately reflect visual content-undermining both accuracy and reliability. Existing methods focus on alignment training or decoding refinements but primarily address symptoms at the generation stage without probing the underlying causes. In this work, we investigate the internal mechanisms driving hallucination in LVLMs, with an emphasis on the multi-head attention module. Specifically, we introduce Vision-aware Head Divergence (VHD), a metric that quantifies the sensitivity of attention head outputs to visual context. Based on this, our findings reveal the presence of vision-aware attention heads that are more attuned to visual information; however, the model’s overreliance on its prior language patterns is closely related to hallucinations. Building on these insights, we propose Vision-aware Head Reinforcement (VHR), a training-free approach to mitigate hallucination by enhancing the role of vision-aware attention heads. Extensive experiments demonstrate that our method achieves superior performance compared to state-of-the-art approaches in mitigating hallucinations, while maintaining high efficiency with negligible additional time overhead.
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[NLP-17] A Rose by Any Other Name: LLM -Generated Explanations Are Good Proxies for Human Explanations to Collect Label Distributions on NLI

【速读】：该论文试图解决的问题是如何利用大型语言模型 (LLMs) 来替代人类生成解释，以近似人类判断分布 (HJD)，从而减少收集每个标签解释的时间成本。解决方案的关键在于使用 LLMs 作为标注者，生成模型解释，并通过实验验证这些解释在自然语言推理 (NLI) 任务中与人类生成的解释具有可比性。研究进一步表明，这种方法不仅适用于包含人类解释的数据集，还能推广到缺乏人类解释的数据集以及具有挑战性的分布外测试集。

链接: https://arxiv.org/abs/2412.13942
作者: Beiduo Chen,Siyao Peng,Anna Korhonen,Barbara Plank
机构: MaiNLP, Center for Information and Language Processing, LMU Munich, Germany; Munich Center for Machine Learning (MCML), Munich, Germany; Language Technology Lab, University of Cambridge, United Kingdom
关键词: human, labeling is ubiquitous, explanations, Disagreement, HJD
类目: Computation and Language (cs.CL)
备注: 25 pages, 21 figures

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Abstract:Disagreement in human labeling is ubiquitous, and can be captured in human judgment distributions (HJDs). Recent research has shown that explanations provide valuable information for understanding human label variation (HLV) and large language models (LLMs) can approximate HJD from a few human-provided label-explanation pairs. However, collecting explanations for every label is still time-consuming. This paper examines whether LLMs can be used to replace humans in generating explanations for approximating HJD. Specifically, we use LLMs as annotators to generate model explanations for a few given human labels. We test ways to obtain and combine these label-explanations with the goal to approximate human judgment distribution. We further compare the resulting human with model-generated explanations, and test automatic and human explanation selection. Our experiments show that LLM explanations are promising for NLI: to estimate HJD, generated explanations yield comparable results to human’s when provided with human labels. Importantly, our results generalize from datasets with human explanations to i) datasets where they are not available and ii) challenging out-of-distribution test sets.
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[NLP-18] Language verY Rare for All

【速读】：该论文试图解决稀有语言（如Monégasque）在机器翻译中的支持问题，特别是由于语料库有限而导致的现有翻译工具无法支持的情况。解决方案的关键在于LYRA（Language verY Rare for All）方法，它结合了开放式大型语言模型（LLM）的微调、检索增强生成（RAG）以及从相关高资源语言的迁移学习。通过这些技术，LYRA能够在单个GPU上进行训练，显著提升了稀有语言翻译的性能，并在实验中展示了其优于现有最先进编码器-解码器模型的效果。

链接: https://arxiv.org/abs/2412.13924
作者: Ibrahim Merad,Amos Wolf,Ziad Mazzawi,Yannick Léo
机构: Kaukana Ventures; Emerton Data
关键词: single GPU, overcome language barriers, expanded machine translation, NLLB, NLLB have expanded
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:In the quest to overcome language barriers, encoder-decoder models like NLLB have expanded machine translation to rare languages, with some models (e.g., NLLB 1.3B) even trainable on a single GPU. While general-purpose LLMs perform well in translation, open LLMs prove highly competitive when fine-tuned for specific tasks involving unknown corpora. We introduce LYRA (Language verY Rare for All), a novel approach that combines open LLM fine-tuning, retrieval-augmented generation (RAG), and transfer learning from related high-resource languages. This study is exclusively focused on single-GPU training to facilitate ease of adoption. Our study focuses on two-way translation between French and Monégasque, a rare language unsupported by existing translation tools due to limited corpus availability. Our results demonstrate LYRA’s effectiveness, frequently surpassing and consistently matching state-of-the-art encoder-decoder models in rare language translation.
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[NLP-19] Pipeline Analysis for Developing Instruct LLM s in Low-Resource Languages: A Case Study on Basque

【速读】：该论文试图解决低资源语言（如巴斯克语）中大型语言模型（LLMs）的优化问题，特别是缩小高资源语言（如英语）与低资源语言之间的性能差距。解决方案的关键在于三个阶段：预训练（pre-training）、指令微调（instruction tuning）和与人类偏好对齐（alignment with human preferences）。通过在高质量的巴斯克语文本上进行持续预训练，模型的自然语言理解（NLU）能力提升了12个百分点；而使用自动翻译数据集进行指令微调和人类偏好对齐，进一步提升了指令跟随性能24个百分点。最终，Llama-eus-8B和Llama-eus-8B-instruct模型在10亿参数以下的类别中为巴斯克语设定了新的技术水平。

链接: https://arxiv.org/abs/2412.13922
作者: Ander Corral,Ixak Sarasua,Xabier Saralegi
机构: Orai NLP Technologies
关键词: Large language models, Large language, exacerbating the gap, typically optimized, optimized for resource-rich
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:Large language models (LLMs) are typically optimized for resource-rich languages like English, exacerbating the gap between high-resource and underrepresented languages. This work presents a detailed analysis of strategies for developing a model capable of following instructions in a low-resource language, specifically Basque, by focusing on three key stages: pre-training, instruction tuning, and alignment with human preferences. Our findings demonstrate that continual pre-training with a high-quality Basque corpus of around 600 million words improves natural language understanding (NLU) of the foundational model by over 12 points. Moreover, instruction tuning and human preference alignment using automatically translated datasets proved highly effective, resulting in a 24-point improvement in instruction-following performance. The resulting models, Llama-eus-8B and Llama-eus-8B-instruct, establish a new state-of-the-art for Basque in the sub-10B parameter category.
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[NLP-20] Understanding and Analyzing Model Robustness and Knowledge-Transfer in Multilingual Neural Machine Translation using TX-Ray

【速读】：该论文试图解决在极低资源环境下多语言神经机器翻译 (Multilingual Neural Machine Translation, MNMT) 中知识迁移的问题。解决方案的关键在于通过预训练模型在英语-英语翻译任务上，并将英语作为所有任务的源语言，随后利用联合多任务学习和顺序迁移学习策略对目标语言对进行微调。这种方法避免了传统方法对特定语言对进行大量预训练的需求，并通过知识迁移提升了模型在极低资源环境下的表现。研究还探讨了神经元剪枝对模型泛化性、鲁棒性和灾难性遗忘的影响，并提出了TX-Ray方法来解释和量化知识迁移。

链接: https://arxiv.org/abs/2412.13881
作者: Vageesh Saxena,Sharid Loáiciga,Nils Rethmeier
机构: 未知
关键词: Neural Machine Translation, Multilingual Neural Machine, Neural Machine, demonstrated significant advancements, Machine Translation
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 103 pages, Master’s thesis

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Abstract:Neural networks have demonstrated significant advancements in Neural Machine Translation (NMT) compared to conventional phrase-based approaches. However, Multilingual Neural Machine Translation (MNMT) in extremely low-resource settings remains underexplored. This research investigates how knowledge transfer across languages can enhance MNMT in such scenarios. Using the Tatoeba translation challenge dataset from Helsinki NLP, we perform English-German, English-French, and English-Spanish translations, leveraging minimal parallel data to establish cross-lingual mappings. Unlike conventional methods relying on extensive pre-training for specific language pairs, we pre-train our model on English-English translations, setting English as the source language for all tasks. The model is fine-tuned on target language pairs using joint multi-task and sequential transfer learning strategies. Our work addresses three key questions: (1) How can knowledge transfer across languages improve MNMT in extremely low-resource scenarios? (2) How does pruning neuron knowledge affect model generalization, robustness, and catastrophic forgetting? (3) How can TX-Ray interpret and quantify knowledge transfer in trained models? Evaluation using BLEU-4 scores demonstrates that sequential transfer learning outperforms baselines on a 40k parallel sentence corpus, showcasing its efficacy. However, pruning neuron knowledge degrades performance, increases catastrophic forgetting, and fails to improve robustness or generalization. Our findings provide valuable insights into the potential and limitations of knowledge transfer and pruning in MNMT for extremely low-resource settings.
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[NLP-21] Crabs: Consuming Resrouce via Auto-generation for LLM -DoS Attack under Black-box Settings

【速读】：该论文试图解决在黑盒设置下对大型语言模型 (LLMs) 进行拒绝服务 (DoS) 攻击的问题。解决方案的关键在于提出了一个自动化算法 AutoDoS，该算法通过引入 DoS 攻击树并优化提示节点覆盖率，以提高在黑盒条件下的攻击效果。AutoDoS 通过语义改进提示节点来增强隐蔽性，并能绕过现有防御机制。此外，论文还揭示了在基本 DoS 提示中植入 Length Trojan 可以提高攻击效率。实验结果表明，AutoDoS 能够将服务响应延迟放大超过 250 倍，导致严重的 GPU 利用率和内存消耗。

链接: https://arxiv.org/abs/2412.13879
作者: Yuanhe Zhang,Zhenhong Zhou,Wei Zhang,Xinyue Wang,Xiaojun Jia,Yang Liu,Sen Su
机构: Beijing University of Posts and Telecommunications; Nanyang Technological University
关键词: Large Language Models, Large Language, Language Models, demonstrated remarkable performance, diverse tasks
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 20 pages, 7 figures, 11 tables

点击查看摘要

Abstract:Large Language Models (LLMs) have demonstrated remarkable performance across diverse tasks. LLMs continue to be vulnerable to external threats, particularly Denial-of-Service (DoS) attacks. Specifically, LLM-DoS attacks aim to exhaust computational resources and block services. However, prior works tend to focus on performing white-box attacks, overlooking black-box settings. In this work, we propose an automated algorithm designed for black-box LLMs, called Auto-Generation for LLM-DoS Attack (AutoDoS). AutoDoS introduces DoS Attack Tree and optimizes the prompt node coverage to enhance effectiveness under black-box conditions. Our method can bypass existing defense with enhanced stealthiness via semantic improvement of prompt nodes. Furthermore, we reveal that implanting Length Trojan in Basic DoS Prompt aids in achieving higher attack efficacy. Experimental results show that AutoDoS amplifies service response latency by over 250 \times \uparrow , leading to severe resource consumption in terms of GPU utilization and memory usage. Our code is available at \urlthis https URL.
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[NLP-22] Energy-Based Preference Model Offers Better Offline Alignment than the Bradley-Terry Preference Model

【速读】：该论文试图解决DPO（Direct Preference Optimization）损失函数在通过KL约束的RLHF（Reinforcement Learning with Human Feedback）损失对齐目标大语言模型（LLM）与人类偏好时，可能存在多个最小值的问题，其中仅有一个最小值满足所需的线性关系。这一问题源于Bradley-Terry偏好模型在最大似然估计（MLE）上不具有唯一性。论文提出的解决方案是引入基于能量的模型（EBM），该模型具有唯一的MLE，从而自然满足线性要求。为在实践中近似MLE，论文提出了对比损失Energy Preference Alignment (EPA)，通过将每个正样本与一个或多个强负样本以及多个弱负样本进行对比，确保在足够多的负样本下，EPA的近似误差几乎必然消失。实验表明，EPA在开放基准测试中持续表现出优于DPO的性能，验证了EBM的优越性。

链接: https://arxiv.org/abs/2412.13862
作者: Yuzhong Hong,Hanshan Zhang,Junwei Bao,Hongfei Jiang,Yang Song
机构: 未知
关键词: reward modeling task, KL-constrained RLHF loss, target LLM, DPO loss, reward modeling
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
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点击查看摘要

Abstract:Since the debut of DPO, it has been shown that aligning a target LLM with human preferences via the KL-constrained RLHF loss is mathematically equivalent to a special kind of reward modeling task. Concretely, the task requires: 1) using the target LLM to parameterize the reward model, and 2) tuning the reward model so that it has a 1:1 linear relationship with the true reward. However, we identify a significant issue: the DPO loss might have multiple minimizers, of which only one satisfies the required linearity condition. The problem arises from a well-known issue of the underlying Bradley-Terry preference model: it does not always have a unique maximum likelihood estimator (MLE). Consequently,the minimizer of the RLHF loss might be unattainable because it is merely one among many minimizers of the DPO loss. As a better alternative, we propose an energy-based model (EBM) that always has a unique MLE, inherently satisfying the linearity requirement. To approximate the MLE in practice, we propose a contrastive loss named Energy Preference Alignment (EPA), wherein each positive sample is contrasted against one or more strong negatives as well as many free weak negatives. Theoretical properties of our EBM enable the approximation error of EPA to almost surely vanish when a sufficient number of negatives are used. Empirically, we demonstrate that EPA consistently delivers better performance on open benchmarks compared to DPO, thereby showing the superiority of our EBM.
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[NLP-23] Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali

【速读】：该论文试图解决在低资源语言（如尼泊尔语）中，如何在不重新训练大型语言模型（LLMs）的情况下，通过领域自适应预训练（DAPT）来持续学习的问题。解决方案的关键在于使用合成数据在4-bit QLoRA设置下继续训练Llama 3 8B模型，使其适应尼泊尔语。通过评估模型的性能、遗忘情况和知识获取能力，研究发现增加评估时的样本数量（shots）可以显著提升最终模型的表现，表明模型在尼泊尔语中具有潜在的知识保留能力。此外，通过层-头自注意力热图分析，进一步验证了模型在尼泊尔语中的依赖解析能力。

链接: https://arxiv.org/abs/2412.13860
作者: Sharad Duwal,Suraj Prasai,Suresh Manandhar
机构: Wiseyak(Wiseyak)
关键词: important research direction, research direction due, retraining large language, Continual learning, large language models
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 10 pages, 2 figures

点击查看摘要

Abstract:Continual learning has emerged as an important research direction due to the infeasibility of retraining large language models (LLMs) from scratch in the event of new data availability. Of great interest is the domain-adaptive pre-training (DAPT) paradigm, which focuses on continually training a pre-trained language model to adapt it to a domain it was not originally trained on. In this work, we evaluate the feasibility of DAPT in a low-resource setting, namely the Nepali language. We use synthetic data to continue training Llama 3 8B to adapt it to the Nepali language in a 4-bit QLoRA setting. We evaluate the adapted model on its performance, forgetting, and knowledge acquisition. We compare the base model and the final model on their Nepali generation abilities, their performance on popular benchmarks, and run case-studies to probe their linguistic knowledge in Nepali. We see some unsurprising forgetting in the final model, but also surprisingly find that increasing the number of shots during evaluation yields better percent increases in the final model (as high as 19.29% increase) compared to the base model (4.98%), suggesting latent retention. We also explore layer-head self-attention heatmaps to establish dependency resolution abilities of the final model in Nepali.
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[NLP-24] RACQUET: Unveiling the Dangers of Overlooked Referential Ambiguity in Visual LLM s

【速读】：该论文试图解决图像问答任务中的指代歧义问题，特别是当前多模态语言模型在处理歧义时表现出的过度自信和由此产生的社会偏见。解决方案的关键在于引入RACQUET数据集，该数据集针对歧义的不同方面进行精心设计，并通过一系列评估揭示了现有模型的局限性。特别是RACQUET-BIAS子集，用于分析模型在未能解决歧义时产生的刻板印象和偏见问题。研究结果强调了为模型配备稳健的歧义处理策略的紧迫性，以避免产生不受欢迎的刻板印象。

链接: https://arxiv.org/abs/2412.13835
作者: Alberto Testoni,Barbara Plank,Raquel Fernández
机构: Institute for Logic, Language and Computation (ILLC), University of Amsterdam; Center for Information and Language Processing, LMU Munich; Munich Center for Machine Learning (MCML), Munich
关键词: effective communication, resolution is key, key to effective, Ambiguity, Ambiguity resolution
类目: Computation and Language (cs.CL)
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点击查看摘要

Abstract:Ambiguity resolution is key to effective communication. While humans effortlessly address ambiguity through conversational grounding strategies, the extent to which current language models can emulate these strategies remains unclear. In this work, we examine referential ambiguity in image-based question answering by introducing RACQUET, a carefully curated dataset targeting distinct aspects of ambiguity. Through a series of evaluations, we reveal significant limitations and problems of overconfidence of state-of-the-art large multimodal language models in addressing ambiguity in their responses. The overconfidence issue becomes particularly relevant for RACQUET-BIAS, a subset designed to analyze a critical yet underexplored problem: failing to address ambiguity leads to stereotypical, socially biased responses. Our results underscore the urgency of equipping models with robust strategies to deal with uncertainty without resorting to undesirable stereotypes.
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[NLP-25] Enhancing Rhetorical Figure Annotation: An Ontology-Based Web Application with RAG Integration COLING2025

【速读】：该论文试图解决在非英语语言（特别是德语）中，针对除隐喻、讽刺和反讽之外的修辞手法（rhetorical figures）进行计算检测时，面临的标注数据不足和合格标注人员缺乏的问题。解决方案的关键在于开发了一个名为“Find your Figure”的网络应用，该应用基于专门为德语修辞手法设计的德国修辞本体（German Rhetorical ontology, GRhOOT），并结合了检索增强生成（Retrieval Augmented Generation, RAG）技术，以提升用户体验和标注效率。论文展示了本体的重构、应用的开发以及内置的RAG流程，并确定了最佳的RAG设置，展示了将修辞本体与RAG结合的实际应用潜力。

链接: https://arxiv.org/abs/2412.13799
作者: Ramona Kühn,Jelena Mitrović,Michael Granitzer
机构: University of Passau(帕绍大学); Institute for AI Research and Development of Serbia(塞尔维亚人工智能研究与发展研究所)
关键词: Rhetorical figures, Rhetorical figures play, German rhetorical figures, play an important, important role
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: The 31st International Conference on Computational Linguistics (COLING 2025)

点击查看摘要

Abstract:Rhetorical figures play an important role in our communication. They are used to convey subtle, implicit meaning, or to emphasize statements. We notice them in hate speech, fake news, and propaganda. By improving the systems for computational detection of rhetorical figures, we can also improve tasks such as hate speech and fake news detection, sentiment analysis, opinion mining, or argument mining. Unfortunately, there is a lack of annotated data, as well as qualified annotators that would help us build large corpora to train machine learning models for the detection of rhetorical figures. The situation is particularly difficult in languages other than English, and for rhetorical figures other than metaphor, sarcasm, and irony. To overcome this issue, we develop a web application called “Find your Figure” that facilitates the identification and annotation of German rhetorical figures. The application is based on the German Rhetorical ontology GRhOOT which we have specially adapted for this purpose. In addition, we improve the user experience with Retrieval Augmented Generation (RAG). In this paper, we present the restructuring of the ontology, the development of the web application, and the built-in RAG pipeline. We also identify the optimal RAG settings for our application. Our approach is one of the first to practically use rhetorical ontologies in combination with RAG and shows promising results.
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[NLP-26] MATCHED: Multimodal Authorship-Attribution To Combat Human Trafficking in Escort-Advertisement Data

【速读】：该论文试图解决人贩子利用在线陪护广告进行匿名受害者宣传的问题，现有的基于文本的归属分析方法（Authorship Attribution, AA）忽略了在线陪护广告的多模态特性，即文本与图像的结合。解决方案的关键在于引入MATCHED数据集，该数据集包含27,619个独特的文本描述和55,115个独特的图像，来源于Backpage陪护平台，覆盖美国四个地理区域的七个城市。通过多任务联合训练目标，论文在供应商识别和验证任务上对纯文本、纯视觉和多模态基线进行了广泛基准测试，发现多模态特征的整合显著提升了分类和检索性能，尤其是在分布内和分布外（OOD）数据集上。尽管文本仍然是主导模态，视觉数据提供了补充的风格线索，增强了模型性能。此外，论文指出，由于陪护广告中文本与图像的语义重叠较低，传统的文本-图像对齐策略（如CLIP和BLIP2）表现不佳，而端到端的多模态训练则更为稳健。这一研究强调了多模态归属分析（Multimodal Authorship Attribution, MAA）在打击人口贩运中的潜力，为执法机构（LEAs）提供了强大的工具来关联广告并破坏贩运网络。

链接: https://arxiv.org/abs/2412.13794
作者: Vageesh Saxena,Benjamin Bashpole,Gijs Van Dijck,Gerasimos Spanakis
机构: Law & Tech Lab(法律与科技实验室); Maastricht University(马斯特里赫特大学); Bashpole Software, Inc.(Bashpole软件公司)
关键词: advertise victims anonymously, traffickers increasingly leveraging, increasingly leveraging online, online escort advertisements, leveraging online escort
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 40 pages

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Abstract:Human trafficking (HT) remains a critical issue, with traffickers increasingly leveraging online escort advertisements (ads) to advertise victims anonymously. Existing detection methods, including Authorship Attribution (AA), often center on text-based analyses and neglect the multimodal nature of online escort ads, which typically pair text with images. To address this gap, we introduce MATCHED, a multimodal dataset of 27,619 unique text descriptions and 55,115 unique images collected from the Backpage escort platform across seven U.S. cities in four geographical regions. Our study extensively benchmarks text-only, vision-only, and multimodal baselines for vendor identification and verification tasks, employing multitask (joint) training objectives that achieve superior classification and retrieval performance on in-distribution and out-of-distribution (OOD) datasets. Integrating multimodal features further enhances this performance, capturing complementary patterns across text and images. While text remains the dominant modality, visual data adds stylistic cues that enrich model performance. Moreover, text-image alignment strategies like CLIP and BLIP2 struggle due to low semantic overlap and vague connections between the modalities of escort ads, with end-to-end multimodal training proving more robust. Our findings emphasize the potential of multimodal AA (MAA) to combat HT, providing LEAs with robust tools to link ads and disrupt trafficking networks.
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[NLP-27] Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models COLING2025

【速读】：该论文试图解决现有大型语言模型（LLMs）在处理物理问题时因知识不足或知识应用错误而失败的问题。解决方案的关键在于提出了一个知识增强框架——Physics Reasoner。该框架通过构建全面的公式集提供明确的物理知识，并利用包含详细指令的检查清单来指导知识的有效应用。具体来说，Physics Reasoner 通过问题分析、公式检索和引导推理三个阶段来解决物理问题，并在分析和推理阶段使用检查清单来增强LLMs的自我改进能力。实验结果表明，该框架有效缓解了知识不足和错误应用的问题，在SciBench上实现了5.8%的平均准确率提升。

链接: https://arxiv.org/abs/2412.13791
作者: Xinyu Pang,Ruixin Hong,Zhanke Zhou,Fangrui Lv,Xinwei Yang,Zhilong Liang,Bo Han,Changshui Zhang
机构: Institute for Artificial Intelligence, Tsinghua University (THUAI); Beijing National Research Center for Information Science and Technology (BNRist); Department of Automation, Tsinghua University, Beijing, P.R.China; TMLR Group, Hong Kong Baptist University
关键词: necessitating complicated reasoning, Physics Reasoner, complicated reasoning ability, Physics problems constitute, Physics
类目: Computation and Language (cs.CL)
备注: COLING 2025

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Abstract:Physics problems constitute a significant aspect of reasoning, necessitating complicated reasoning ability and abundant physics knowledge. However, existing large language models (LLMs) frequently fail due to a lack of knowledge or incorrect knowledge application. To mitigate these issues, we propose Physics Reasoner, a knowledge-augmented framework to solve physics problems with LLMs. Specifically, the proposed framework constructs a comprehensive formula set to provide explicit physics knowledge and utilizes checklists containing detailed instructions to guide effective knowledge application. Namely, given a physics problem, Physics Reasoner solves it through three stages: problem analysis, formula retrieval, and guided reasoning. During the process, checklists are employed to enhance LLMs’ self-improvement in the analysis and reasoning stages. Empirically, Physics Reasoner mitigates the issues of insufficient knowledge and incorrect application, achieving state-of-the-art performance on SciBench with an average accuracy improvement of 5.8%.
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[NLP-28] Open Universal Arabic ASR Leaderboard

【速读】：该论文试图解决阿拉伯语自动语音识别（ASR）模型在多方言数据集上的性能评估和通用性问题。解决方案的关键在于引入了一个名为“Open Universal Arabic ASR Leaderboard”的持续基准项目，该基准项目旨在评估开源通用阿拉伯语ASR模型在多种方言数据集上的表现。通过提供对模型鲁棒性、说话人适应性、推理效率和内存消耗的全面分析，论文为阿拉伯语ASR社区提供了一个通用的评估框架，并帮助研究人员了解模型在多方言环境下的泛化能力。

链接: https://arxiv.org/abs/2412.13788
作者: Yingzhi Wang,Anas Alhmoud,Muhammad Alqurishi
机构: 未知
关键词: Arabic ASR, pushed Arabic ASR, Arabic ASR models, increasingly pushed Arabic, Arabic ASR Leaderboard
类目: Computation and Language (cs.CL)
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Abstract:In recent years, the enhanced capabilities of ASR models and the emergence of multi-dialect datasets have increasingly pushed Arabic ASR model development toward an all-dialect-in-one direction. This trend highlights the need for benchmarking studies that evaluate model performance on multiple dialects, providing the community with insights into models’ generalization capabilities. In this paper, we introduce Open Universal Arabic ASR Leaderboard, a continuous benchmark project for open-source general Arabic ASR models across various multi-dialect datasets. We also provide a comprehensive analysis of the model’s robustness, speaker adaptation, inference efficiency, and memory consumption. This work aims to offer the Arabic ASR community a reference for models’ general performance and also establish a common evaluation framework for multi-dialectal Arabic ASR models. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2412.13788 [cs.CL] (or arXiv:2412.13788v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2412.13788 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[NLP-29] Knowledge Editing with Dynamic Knowledge Graphs for Multi-hop Question Answering AAAI2025

【速读】：该论文试图解决多跳问答 (Multi-hop Question Answering, MHQA) 中大型语言模型 (LLMs) 面临的广泛知识需求和知识冲突问题。解决方案的关键在于提出了一种名为 KEDKG 的新型知识编辑方法，该方法通过动态知识图谱 (Dynamic Knowledge Graph) 来确保答案的可靠性。KEDKG 的核心步骤包括动态知识图谱构建和知识图谱增强生成。首先，KEDKG 自主构建动态知识图谱以存储修订信息并解决潜在的知识冲突；其次，通过细粒度检索策略和实体与关系检测器来提高图谱检索的准确性，从而增强 LLM 的生成能力。实验结果表明，KEDKG 在动态信息环境下的表现优于现有最先进模型，提供了更准确和可靠的答案。

链接: https://arxiv.org/abs/2412.13782
作者: Yifan Lu,Yigeng Zhou,Jing Li,Yequan Wang,Xuebo Liu,Daojing He,Fangming Liu,Min Zhang
机构: 未知
关键词: Multi-hop question answering, Multi-hop question, knowledge demands involved, extensive knowledge demands, large language models
类目: Computation and Language (cs.CL)
备注: AAAI 2025

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Abstract:Multi-hop question answering (MHQA) poses a significant challenge for large language models (LLMs) due to the extensive knowledge demands involved. Knowledge editing, which aims to precisely modify the LLMs to incorporate specific knowledge without negatively impacting other unrelated knowledge, offers a potential solution for addressing MHQA challenges with LLMs. However, current solutions struggle to effectively resolve issues of knowledge conflicts. Most parameter-preserving editing methods are hindered by inaccurate retrieval and overlook secondary editing issues, which can introduce noise into the reasoning process of LLMs. In this paper, we introduce KEDKG, a novel knowledge editing method that leverages a dynamic knowledge graph for MHQA, designed to ensure the reliability of answers. KEDKG involves two primary steps: dynamic knowledge graph construction and knowledge graph augmented generation. Initially, KEDKG autonomously constructs a dynamic knowledge graph to store revised information while resolving potential knowledge conflicts. Subsequently, it employs a fine-grained retrieval strategy coupled with an entity and relation detector to enhance the accuracy of graph retrieval for LLM generation. Experimental results on benchmarks show that KEDKG surpasses previous state-of-the-art models, delivering more accurate and reliable answers in environments with dynamic information.
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[NLP-30] Meta-Reflection: A Feedback-Free Reflection Learning Framework

【速读】：该论文试图解决大语言模型 (LLMs) 在自然语言理解和推理中常出现的幻觉 (hallucinations) 和不忠实推理 (unfaithful reasoning) 问题。解决方案的关键在于提出了一种无需外部反馈的反射机制，称为元反射 (Meta-Reflection)。该机制通过将历史反思见解集成到代码本 (codebook) 中，使得模型能够在遇到类似问题时直接检索和利用这些历史见解，从而在单次推理过程中实现对响应的优化，而不需要依赖多轮迭代或多代理推理过程。

链接: https://arxiv.org/abs/2412.13781
作者: Yaoke Wang,Yun Zhu,Xintong Bao,Wenqiao Zhang,Suyang Dai,Kehan Chen,Wenqiang Li,Gang Huang,Siliang Tang,Yueting Zhuang
机构: 未知
关键词: large language models, natural language understanding, display undesirable behaviors, unfaithful reasoning, language models
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:Despite the remarkable capabilities of large language models (LLMs) in natural language understanding and reasoning, they often display undesirable behaviors, such as generating hallucinations and unfaithful reasoning. A prevalent strategy to mitigate these issues is the use of reflection, which refines responses through an iterative process. However, while promising, reflection heavily relies on high-quality external feedback and requires iterative multi-agent inference processes, thus hindering its practical application. In this paper, we propose Meta-Reflection, a novel feedback-free reflection mechanism that necessitates only a single inference pass without external feedback. Motivated by the human ability to remember and retrieve reflections from past experiences when encountering similar problems, Meta-Reflection integrates reflective insights into a codebook, allowing the historical insights to be stored, retrieved, and used to guide LLMs in problem-solving. To thoroughly investigate and evaluate the practicality of Meta-Reflection in real-world scenarios, we introduce an industrial e-commerce benchmark named E-commerce Customer Intent Detection (ECID). Extensive experiments conducted on both public datasets and the ECID benchmark highlight the effectiveness and efficiency of our proposed approach.
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[NLP-31] Semantic Convergence: Harmonizing Recommender Systems via Two-Stage Alignment and Behavioral Semantic Tokenization AAAI2025

【速读】：该论文试图解决推荐系统中稀疏的协同语义与大型语言模型（LLMs）中密集的token表示之间的不一致问题。解决方案的关键在于提出了一种新颖的框架，通过Alignment Tokenization模块将ItemIDs转换为与LLMs语义空间对齐的序列，并设计了一系列监督学习任务来对齐协同信号与自然语言语义的细微差别。此外，通过预缓存每个用户的top-K结果来优化在线推理，减少延迟并提高效率。实验结果表明，该模型显著提升了召回率指标，并展示了推荐系统的良好扩展性。

链接: https://arxiv.org/abs/2412.13771
作者: Guanghan Li,Xun Zhang,Yufei Zhang,Yifan Yin,Guojun Yin,Wei Lin
机构: 未知
关键词: exceptional reasoning capabilities, Large language models, discerning profound user, profound user interests, endowed with exceptional
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 7 pages, 3 figures, AAAI 2025

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Abstract:Large language models (LLMs), endowed with exceptional reasoning capabilities, are adept at discerning profound user interests from historical behaviors, thereby presenting a promising avenue for the advancement of recommendation systems. However, a notable discrepancy persists between the sparse collaborative semantics typically found in recommendation systems and the dense token representations within LLMs. In our study, we propose a novel framework that harmoniously merges traditional recommendation models with the prowess of LLMs. We initiate this integration by transforming ItemIDs into sequences that align semantically with the LLMs space, through the proposed Alignment Tokenization module. Additionally, we design a series of specialized supervised learning tasks aimed at aligning collaborative signals with the subtleties of natural language semantics. To ensure practical applicability, we optimize online inference by pre-caching the top-K results for each user, reducing latency and improving effciency. Extensive experimental evidence indicates that our model markedly improves recall metrics and displays remarkable scalability of recommendation systems.
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[NLP-32] LLM -SEM: A Sentiment-Based Student Engagement Metric Using LLM S for E-Learning Platforms

【速读】：该论文试图解决现有电子学习平台中学生参与度分析方法的局限性，包括处理文本评论中的模糊情感和依赖有限元数据的问题。解决方案的关键在于引入LLM-SEM（基于语言模型的学生参与度指标），通过利用视频元数据和学生评论的情感分析来衡量参与度。该方法利用大型语言模型（LLMs）生成高质量的情感预测，以缓解文本模糊性，并通过标准化关键特征（如观看次数和点赞数）来提升准确性。LLM-SEM结合全面的元数据和情感极性分数，能够在课程和课时层面全面评估学生参与度，并通过实验验证了其在可扩展性和准确性方面的有效性。

链接: https://arxiv.org/abs/2412.13765
作者: Ali Hamdi,Ahmed Abdelmoneim Mazrou,Mohamed Shaltout
机构: 未知
关键词: including automated systems, handling fuzzy sentiment, analyzing student engagement, Current methods, e-learning platforms
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:Current methods for analyzing student engagement in e-learning platforms, including automated systems, often struggle with challenges such as handling fuzzy sentiment in text comments and relying on limited metadata. Traditional approaches, such as surveys and questionnaires, also face issues like small sample sizes and scalability. In this paper, we introduce LLM-SEM (Language Model-Based Student Engagement Metric), a novel approach that leverages video metadata and sentiment analysis of student comments to measure engagement. By utilizing recent Large Language Models (LLMs), we generate high-quality sentiment predictions to mitigate text fuzziness and normalize key features such as views and likes. Our holistic method combines comprehensive metadata with sentiment polarity scores to gauge engagement at both the course and lesson levels. Extensive experiments were conducted to evaluate various LLM models, demonstrating the effectiveness of LLM-SEM in providing a scalable and accurate measure of student engagement. We fine-tuned LLMs, including AraBERT, TXLM-RoBERTa, LLama 3B and Gemma 9B from Ollama, using human-annotated sentiment datasets to enhance prediction accuracy.
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[NLP-33] RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment

【速读】：该论文试图解决现有检索增强语言模型 (RALMs) 在偏好对齐方面的问题，即如何评估和选择可靠的奖励模型 (RMs) 来引导优化过程以更好地对齐人类偏好。解决方案的关键在于提出了RAG-RewardBench，这是首个用于评估RAG设置中RMs的基准。该基准通过设计四个关键且具有挑战性的RAG特定场景（多跳推理、细粒度引用、适当放弃和冲突鲁棒性）来评估RMs，并结合多样化的数据源（18个RAG子集、6个检索器和24个RALMs）。此外，采用LLM-as-a-judge方法提高偏好标注的效率和效果，显示出与人类标注的高度相关性。通过RAG-RewardBench，论文对45个RMs进行了全面评估，揭示了它们在RAG场景中的局限性，并强调了现有RALMs在偏好对齐方面几乎没有改进，指出了未来需要转向偏好对齐的方向。

链接: https://arxiv.org/abs/2412.13746
作者: Zhuoran Jin,Hongbang Yuan,Tianyi Men,Pengfei Cao,Yubo Chen,Kang Liu,Jun Zhao
机构: School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; The Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
关键词: significant progress made, retrieval augmented language, providing trustworthy responses, augmented language models, overlook effective alignment
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: 26 pages, 12 figures, 6 tables

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Abstract:Despite the significant progress made by existing retrieval augmented language models (RALMs) in providing trustworthy responses and grounding in reliable sources, they often overlook effective alignment with human preferences. In the alignment process, reward models (RMs) act as a crucial proxy for human values to guide optimization. However, it remains unclear how to evaluate and select a reliable RM for preference alignment in RALMs. To this end, we propose RAG-RewardBench, the first benchmark for evaluating RMs in RAG settings. First, we design four crucial and challenging RAG-specific scenarios to assess RMs, including multi-hop reasoning, fine-grained citation, appropriate abstain, and conflict robustness. Then, we incorporate 18 RAG subsets, six retrievers, and 24 RALMs to increase the diversity of data sources. Finally, we adopt an LLM-as-a-judge approach to improve preference annotation efficiency and effectiveness, exhibiting a strong correlation with human annotations. Based on the RAG-RewardBench, we conduct a comprehensive evaluation of 45 RMs and uncover their limitations in RAG scenarios. Additionally, we also reveal that existing trained RALMs show almost no improvement in preference alignment, highlighting the need for a shift towards preference-aligned this http URL release our benchmark and code publicly at this https URL for future work.
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[NLP-34] Learning Complex Word Embeddings in Classical and Quantum Spaces

【速读】：该论文试图解决在自然语言处理 (NLP) 中生成复数值词嵌入 (complex-valued word embeddings) 的问题，特别是在量子启发模型中的应用。解决方案的关键在于提出了一种基于经典Skip-gram模型的复数值词嵌入训练方法，并通过参数化量子电路 (Parameterised Quantum Circuits, PQCs) 生成归一化的复数向量。论文还开发了高效的C代码实现，使得能够在38亿词的语料库上训练超过40万词汇量的复数值嵌入，并为每个词汇单独训练PQC。通过两阶段的训练过程，量子词嵌入在标准相似性和相关性数据集上表现与经典Skip-gram嵌入相当，展示了在复数空间中学习嵌入的可扩展性和潜在优势。

链接: https://arxiv.org/abs/2412.13745
作者: Carys Harvey,Stephen Clark,Douglas Brown,Konstantinos Meichanetzidis
机构: Quantinuum; Quantum Engineering Centre for Doctoral Training (量子工程博士培训中心), University of Bristol (布里斯托大学)
关键词: straightforward adaptation simply, adaptation simply replacing, classical Skip-gram embeddings, classical Skip-gram model, present a variety
类目: Computation and Language (cs.CL)
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Abstract:We present a variety of methods for training complex-valued word embeddings, based on the classical Skip-gram model, with a straightforward adaptation simply replacing the real-valued vectors with arbitrary vectors of complex numbers. In a more “physically-inspired” approach, the vectors are produced by parameterised quantum circuits (PQCs), which are unitary transformations resulting in normalised vectors which have a probabilistic interpretation. We develop a complex-valued version of the highly optimised C code version of Skip-gram, which allows us to easily produce complex embeddings trained on a 3.8B-word corpus for a vocabulary size of over 400k, for which we are then able to train a separate PQC for each word. We evaluate the complex embeddings on a set of standard similarity and relatedness datasets, for some models obtaining results competitive with the classical baseline. We find that, while training the PQCs directly tends to harm performance, the quantum word embeddings from the two-stage process perform as well as the classical Skip-gram embeddings with comparable numbers of parameters. This enables a highly scalable route to learning embeddings in complex spaces which scales with the size of the vocabulary rather than the size of the training corpus. In summary, we demonstrate how to produce a large set of high-quality word embeddings for use in complex-valued and quantum-inspired NLP models, and for exploring potential advantage in quantum NLP models.
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[NLP-35] Federated Learning and RAG Integration: A Scalable Approach for Medical Large Language Models

【速读】：该论文试图解决在医疗领域中开发特定领域的大型语言模型 (Large Language Models, LLMs) 时面临的性能优化和数据隐私保护问题。解决方案的关键在于将检索增强生成 (Retrieval-Augmented Generation, RAG) 系统与联邦学习 (Federated Learning) 框架相结合。通过利用联邦学习的分布式计算和数据隐私保护优势，以及RAG系统在文本生成中的增强作用，研究实现了在不同客户端配置下训练的模型性能优化。实验结果表明，这种集成方法在所有评估指标上均优于未集成的模型，为医疗领域的LLMs开发提供了一种可扩展且隐私保护的解决方案。

链接: https://arxiv.org/abs/2412.13720
作者: Jincheol Jung,Hongju Jeong,Eui-Nam Huh
机构: College of Software Convergence, Kyung Hee University (软件融合学院，庆熙大学); Kyung Hee University (庆熙大学)
关键词: Large Language Models, domain-specific Large Language, Large Language, integrating Retrieval-Augmented Generation, federated learning framework
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:This study analyzes the performance of domain-specific Large Language Models (LLMs) for the medical field by integrating Retrieval-Augmented Generation (RAG) systems within a federated learning framework. Leveraging the inherent advantages of federated learning, such as preserving data privacy and enabling distributed computation, this research explores the integration of RAG systems with models trained under varying client configurations to optimize performance. Experimental results demonstrate that the federated learning-based models integrated with RAG systems consistently outperform their non-integrated counterparts across all evaluation metrics. This study highlights the potential of combining federated learning and RAG systems for developing domain-specific LLMs in the medical field, providing a scalable and privacy-preserving solution for enhancing text generation capabilities.
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[NLP-36] owards Automatic Evaluation for Image Transcreation

【速读】：该论文试图解决图像转译（transcreation）自动化评估的问题，特别是缺乏自动评估机制阻碍了将其定义为正式的机器学习（ML）问题。解决方案的关键在于提出了一套受机器翻译（MT）指标启发的自动评估指标，分为基于对象（Object-based）、基于嵌入（Embedding-based）和基于视觉语言模型（VLM-based）三类，并根据文化相关性、语义等价性和视觉相似性三个关键维度设计了评估系统。研究结果表明，专有视觉语言模型在识别文化相关性和语义等价性方面表现最佳，而视觉编码器在测量视觉相似性方面表现出色。通过跨7个国家的元评估，这些指标与人类评分显示出强烈的一致性，平均段级相关性在0.55到0.87之间。论文最终提供了一个基于理论基础和实际应用的自动化图像转译评估框架。

链接: https://arxiv.org/abs/2412.13717
作者: Simran Khanuja,Vivek Iyer,Claire He,Graham Neubig
机构: Carnegie Mellon University(卡内基梅隆大学); University of Edinburgh(爱丁堡大学)
关键词: speech and text, formal Machine Learning, conventional paradigms, paradigms of translating, translating speech
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Beyond conventional paradigms of translating speech and text, recently, there has been interest in automated transcreation of images to facilitate localization of visual content across different cultures. Attempts to define this as a formal Machine Learning (ML) problem have been impeded by the lack of automatic evaluation mechanisms, with previous work relying solely on human evaluation. In this paper, we seek to close this gap by proposing a suite of automatic evaluation metrics inspired by machine translation (MT) metrics, categorized into: a) Object-based, b) Embedding-based, and c) VLM-based. Drawing on theories from translation studies and real-world transcreation practices, we identify three critical dimensions of image transcreation: cultural relevance, semantic equivalence and visual similarity, and design our metrics to evaluate systems along these axes. Our results show that proprietary VLMs best identify cultural relevance and semantic equivalence, while vision-encoder representations are adept at measuring visual similarity. Meta-evaluation across 7 countries shows our metrics agree strongly with human ratings, with average segment-level correlations ranging from 0.55-0.87. Finally, through a discussion of the merits and demerits of each metric, we offer a robust framework for automated image transcreation evaluation, grounded in both theoretical foundations and practical application. Our code can be found here: this https URL
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[NLP-37] Mitigating Adversarial Attacks in LLM s through Defensive Suffix Generation

【速读】：该论文试图解决大语言模型 (Large Language Models, LLMs) 在自然语言处理任务中易受对抗攻击的问题，即输入的微小扰动可能导致有害或误导性输出。解决方案的关键在于设计了一种基于梯度的防御性后缀生成算法，通过在输入提示中附加经过精心优化的防御性后缀 (defensive suffixes) 来增强模型的鲁棒性。该算法的核心创新在于引入了一个新的总损失函数 (total loss function, L_total)，该函数结合了防御性损失 (defensive loss, L_def) 和对抗性损失 (adversarial loss, L_adv)，从而更有效地生成防御性后缀。实验结果表明，该方法在多个开源 LLMs 上显著降低了攻击成功率 (Attack Success Rate, ASR)，并提升了模型的困惑度 (perplexity) 和 TruthfulQA 评估中的真实性得分 (Truthfulness scores)。

链接: https://arxiv.org/abs/2412.13705
作者: Minkyoung Kim,Yunha Kim,Hyeram Seo,Heejung Choi,Jiye Han,Gaeun Kee,Soyoung Ko,HyoJe Jung,Byeolhee Kim,Young-Hak Kim,Sanghyun Park,Tae Joon Jun
机构: Department of Information Medicine, Asan Medical Center; Department of Computer Science, Yonsei University; Department of Artificial Intelligence, Yonsei University; Division of Cardiology, Department of Information Medicine, Asan Medical Center, University of Ulsan College of Medicine; Big Data Research Center, Asan Institute for Life Sciences
关键词: Large language models, language processing tasks, natural language processing, exhibited outstanding performance, Large language
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:Large language models (LLMs) have exhibited outstanding performance in natural language processing tasks. However, these models remain susceptible to adversarial attacks in which slight input perturbations can lead to harmful or misleading outputs. A gradient-based defensive suffix generation algorithm is designed to bolster the robustness of LLMs. By appending carefully optimized defensive suffixes to input prompts, the algorithm mitigates adversarial influences while preserving the models’ utility. To enhance adversarial understanding, a novel total loss function ( L_\texttotal ) combining defensive loss ( L_\textdef ) and adversarial loss ( L_\textadv ) generates defensive suffixes more effectively. Experimental evaluations conducted on open-source LLMs such as Gemma-7B, mistral-7B, Llama2-7B, and Llama2-13B show that the proposed method reduces attack success rates (ASR) by an average of 11% compared to models without defensive suffixes. Additionally, the perplexity score of Gemma-7B decreased from 6.57 to 3.93 when applying the defensive suffix generated by openELM-270M. Furthermore, TruthfulQA evaluations demonstrate consistent improvements with Truthfulness scores increasing by up to 10% across tested configurations. This approach significantly enhances the security of LLMs in critical applications without requiring extensive retraining.
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[NLP-38] yphoon 2: A Family of Open Text and Multimodal Thai Large Language Models

【速读】：该论文旨在解决泰语在文本、视觉和音频领域的多模态大语言模型优化问题。解决方案的关键在于开发了Typhoon 2系列模型，包括Typhoon2-Text、Typhoon2-Vision和Typhoon2-Audio。Typhoon2-Text通过在Llama 3和Qwen2等先进开源模型的基础上进行持续预训练，并结合英语和泰语数据，采用多种后训练技术来提升泰语性能，同时保留基础模型的原始能力。Typhoon2-Vision专注于提升泰语文档理解能力，同时保持通用视觉能力。Typhoon2-Audio则引入了一种端到端的语音到语音模型架构，能够处理音频、语音和文本输入，并同时生成文本和语音输出。

链接: https://arxiv.org/abs/2412.13702
作者: Kunat Pipatanakul,Potsawee Manakul,Natapong Nitarach,Warit Sirichotedumrong,Surapon Nonesung,Teetouch Jaknamon,Parinthapat Pengpun,Pittawat Taveekitworachai,Adisai Na-Thalang,Sittipong Sripaisarnmongkol,Krisanapong Jirayoot,Kasima Tharnpipitchai
机构: SCB 10X, SCBX; OpenAI
关键词: paper introduces Typhoon, multimodal large language, introduces Typhoon, multimodal large, large language models
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: technical report, 55 pages

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Abstract:This paper introduces Typhoon 2, a series of text and multimodal large language models optimized for the Thai language. The series includes models for text, vision, and audio. Typhoon2-Text builds on state-of-the-art open models, such as Llama 3 and Qwen2, and we perform continual pre-training on a mixture of English and Thai data. We employ various post-training techniques to enhance Thai language performance while preserving the base models’ original capabilities. We release text models across a range of sizes, from 1 to 70 billion parameters, available in both base and instruction-tuned variants. Typhoon2-Vision improves Thai document understanding while retaining general visual capabilities, such as image captioning. Typhoon2-Audio introduces an end-to-end speech-to-speech model architecture capable of processing audio, speech, and text inputs and generating both text and speech outputs simultaneously.
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[NLP-39] owards Efficient and Explainable Hate Speech Detection via Model Distillation

【速读】：该论文试图解决在线仇恨和辱骂语言的自动检测问题，特别是当前模型缺乏可解释性和可解释性的挑战。解决方案的关键在于通过Chain-of-Thought方法从大型语言模型（LLMs）中提取解释，并将其蒸馏到小型语言模型中。这种方法不仅提高了分类性能，还保持了与大型模型相同的解释质量，从而使仇恨言论检测更加经济、易懂且可操作。

链接: https://arxiv.org/abs/2412.13698
作者: Paloma Piot,Javier Parapar
机构: 未知
关键词: Automatic detection, online spread, essential to combat, combat its online, Automatic
类目: Computation and Language (cs.CL)
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Abstract:Automatic detection of hate and abusive language is essential to combat its online spread. Moreover, recognising and explaining hate speech serves to educate people about its negative effects. However, most current detection models operate as black boxes, lacking interpretability and explainability. In this context, Large Language Models (LLMs) have proven effective for hate speech detection and to promote interpretability. Nevertheless, they are computationally costly to run. In this work, we propose distilling big language models by using Chain-of-Thought to extract explanations that support the hate speech classification task. Having small language models for these tasks will contribute to their use in operational settings. In this paper, we demonstrate that distilled models deliver explanations of the same quality as larger models while surpassing them in classification performance. This dual capability, classifying and explaining, advances hate speech detection making it more affordable, understandable and actionable.
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[NLP-40] Discerning and Characterising Types of Competency Questions for Ontologies

【速读】：该论文试图解决在本体开发中，能力问题（Competency Questions, CQs）的制定和评估缺乏明确指导的问题。解决方案的关键在于提出了一个能力问题模型，该模型包含五种主要类型的能力问题：范围问题（Scoping Questions, SCQ）、验证问题（Validating Questions, VCQ）、基础问题（Foundational Questions, FCQ）、关系问题（Relationship Questions, RCQ）和元属性问题（Metaproperty Questions, MpCQ）。每种类型的问题都有其特定的目的，从而增强了能力问题的清晰度，并提高了在本体开发中的有效性。此外，论文还创建了一个包含438个能力问题的注释库（ROCQS），以进一步展示和区分不同类型的能力问题，促进其应用和研究。

链接: https://arxiv.org/abs/2412.13688
作者: C. Maria Keet,Zubeida Casmod Khan
机构: 未知
关键词: Competency Questions, Ontology Competency QuestionS, ontology development, CQs, ontology development tasks
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 16 pages, 5 figures

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Abstract:Competency Questions (CQs) are widely used in ontology development by guiding, among others, the scoping and validation stages. However, very limited guidance exists for formulating CQs and assessing whether they are good CQs, leading to issues such as ambiguity and unusable formulations. To solve this, one requires insight into the nature of CQs for ontologies and their constituent parts, as well as which ones are not. We aim to contribute to such theoretical foundations in this paper, which is informed by analysing questions, their uses, and the myriad of ontology development tasks. This resulted in a first Model for Competency Questions, which comprises five main types of CQs, each with a different purpose: Scoping (SCQ), Validating (VCQ), Foundational (FCQ), Relationship (RCQ), and Metaproperty (MpCQ) questions. This model enhances the clarity of CQs and therewith aims to improve on the effectiveness of CQs in ontology development, thanks to their respective identifiable distinct constituent elements. We illustrate and evaluate them with a user story and demonstrate where which type can be used in ontology development tasks. To foster use and research, we created an annotated repository of 438 CQs, the Repository of Ontology Competency QuestionS (ROCQS), incorporating an existing CQ dataset and new CQs and CQ templates, which further demonstrate distinctions among types of CQs.
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[NLP-41] ChinaTravel: A Real-World Benchmark for Language Agents in Chinese Travel Planning WWW

【速读】：该论文试图解决现有基准测试无法反映真实世界旅行规划需求的多样性和复杂性问题。解决方案的关键在于引入ChinaTravel基准，该基准通过问卷收集真实的旅行需求，并提出一种可组合的领域特定语言，以实现可扩展的评估过程，涵盖可行性、约束满足和偏好比较。这一方法显著提升了神经符号代理在旅行规划中的表现，约束满足率达到27.9%，远超纯神经模型的2.6%。此外，论文还识别了实际部署中的关键挑战，如开放语言推理和未见概念组合，强调了ChinaTravel在推动复杂现实场景中语言代理发展的重要作用。

链接: https://arxiv.org/abs/2412.13682
作者: Jie-Jing Shao,Xiao-Wen Yang,Bo-Wen Zhang,Baizhi Chen,Wen-Da Wei,Lan-Zhe Guo,Yu-feng Li
机构: National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China; School of Artificial Intelligence, Nanjing University, Nanjing, China; School of Intelligence Science and Technology, Nanjing University, Nanjing, China
关键词: Recent advances, advances in LLMs, tool integration, rapidly sparked, Recent
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:Recent advances in LLMs, particularly in language reasoning and tool integration, have rapidly sparked the real-world development of Language Agents. Among these, travel planning represents a prominent domain, combining academic challenges with practical value due to its complexity and market demand. However, existing benchmarks fail to reflect the diverse, real-world requirements crucial for deployment. To address this gap, we introduce ChinaTravel, a benchmark specifically designed for authentic Chinese travel planning scenarios. We collect the travel requirements from questionnaires and propose a compositionally generalizable domain-specific language that enables a scalable evaluation process, covering feasibility, constraint satisfaction, and preference comparison. Empirical studies reveal the potential of neuro-symbolic agents in travel planning, achieving a constraint satisfaction rate of 27.9%, significantly surpassing purely neural models at 2.6%. Moreover, we identify key challenges in real-world travel planning deployments, including open language reasoning and unseen concept composition. These findings highlight the significance of ChinaTravel as a pivotal milestone for advancing language agents in complex, real-world planning scenarios.
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[NLP-42] Clio: Privacy-Preserving Insights into Real-World AI Use

【速读】：该论文试图解决如何在不侵犯用户隐私的前提下，分析AI助手在现实世界中的使用情况。解决方案的关键是提出了Clio（Claude insights and observations）平台，该平台利用AI助手自身来分析和汇总数百万次对话中的使用模式，而无需人工审查原始对话内容。通过这种方式，Clio能够在保护隐私的同时，准确地识别AI助手的实际应用场景（如编码、写作和研究任务）以及跨语言的使用差异，并帮助提升系统的安全性，例如检测系统滥用行为和监控未知风险。

链接: https://arxiv.org/abs/2412.13678
作者: Alex Tamkin,Miles McCain,Kunal Handa,Esin Durmus,Liane Lovitt,Ankur Rathi,Saffron Huang,Alfred Mountfield,Jerry Hong,Stuart Ritchie,Michael Stern,Brian Clarke,Landon Goldberg,Theodore R. Sumers,Jared Mueller,William McEachen,Wes Mitchell,Shan Carter,Jack Clark,Jared Kaplan,Deep Ganguli
机构: Anthropic; OpenAI
关键词: http URL Free, Clio, http URL, real world, conversations
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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Abstract:How are AI assistants being used in the real world? While model providers in theory have a window into this impact via their users’ data, both privacy concerns and practical challenges have made analyzing this data difficult. To address these issues, we present Clio (Claude insights and observations), a privacy-preserving platform that uses AI assistants themselves to analyze and surface aggregated usage patterns across millions of conversations, without the need for human reviewers to read raw conversations. We validate this can be done with a high degree of accuracy and privacy by conducting extensive evaluations. We demonstrate Clio’s usefulness in two broad ways. First, we share insights about how models are being used in the real world from one million this http URL Free and Pro conversations, ranging from providing advice on hairstyles to providing guidance on Git operations and concepts. We also identify the most common high-level use cases on this http URL (coding, writing, and research tasks) as well as patterns that differ across languages (e.g., conversations in Japanese discuss elder care and aging populations at higher-than-typical rates). Second, we use Clio to make our systems safer by identifying coordinated attempts to abuse our systems, monitoring for unknown unknowns during critical periods like launches of new capabilities or major world events, and improving our existing monitoring systems. We also discuss the limitations of our approach, as well as risks and ethical concerns. By enabling analysis of real-world AI usage, Clio provides a scalable platform for empirically grounded AI safety and governance.
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[NLP-43] AntiLeak-Bench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge

【速读】：该论文试图解决大语言模型（LLM）评估中的数据污染问题，即测试数据被引入到新模型的训练集中，从而影响评估的公平性。解决方案的关键在于提出了AntiLeak-Bench，一个自动化的反泄漏基准测试框架。与现有方法不同，AntiLeak-Bench通过构建明确不包含在LLM训练集中的新知识样本，确保严格的污染评估。此外，该框架设计了一个全自动的工作流程，无需人工干预即可构建和更新基准，从而显著降低了基准维护的成本，适应不断涌现的LLM。实验结果表明，数据污染在LLM的截止时间之前可能已经存在，而AntiLeak-Bench能够有效克服这一挑战。

链接: https://arxiv.org/abs/2412.13670
作者: Xiaobao Wu,Liangming Pan,Yuxi Xie,Ruiwen Zhou,Shuai Zhao,Yubo Ma,Mingzhe Du,Rui Mao,Anh Tuan Luu,William Yang Wang
机构: Nanyang Technological University; University of California, Santa Barbara; National University of Singapore; Shanghai Jiao Tong University; University of Arizona
关键词: introducing test data, newly collected data, hinders fair LLM, newer models’ training, contamination hinders fair
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:Data contamination hinders fair LLM evaluation by introducing test data into newer models’ training sets. Existing studies solve this challenge by updating benchmarks with newly collected data. However, they fail to guarantee contamination-free evaluation as the newly collected data may contain pre-existing knowledge, and their benchmark updates rely on intensive human labor. To address these issues, we in this paper propose AntiLeak-Bench, an automated anti-leakage benchmarking framework. Instead of simply using newly collected data, we construct samples with explicitly new knowledge absent from LLMs’ training sets, which thus ensures strictly contamination-free evaluation. We further design a fully automated workflow to build and update our benchmark without human labor. This significantly reduces the cost of benchmark maintenance to accommodate emerging LLMs. Through extensive experiments, we highlight that data contamination likely exists before LLMs’ cutoff time and demonstrate AntiLeak-Bench effectively overcomes this challenge.
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[NLP-44] Evaluation of LLM Vulnerabilities to Being Misused for Personalized Disinformation Generation

【速读】：该论文试图解决生成式大型语言模型 (LLMs) 在生成个性化虚假新闻文章方面的潜在滥用问题。研究的关键在于评估当前开源和闭源 LLMs 的漏洞，以及它们生成个性化虚假新闻的倾向性。论文进一步探讨了这些模型是否能够可靠地元评估个性化质量，并分析个性化对生成文本可检测性的影响。研究结果表明，现有的安全过滤器和免责声明在大多数评估的 LLMs 中未能有效发挥作用，且个性化实际上降低了安全过滤器的激活，起到了“越狱”效果。因此，解决方案的关键在于 LLM 开发者和提供商需要紧急加强安全过滤机制，以应对这一风险。

链接: https://arxiv.org/abs/2412.13666
作者: Aneta Zugecova,Dominik Macko,Ivan Srba,Robert Moro,Jakub Kopal,Katarina Marcincinova,Matus Mesarcik
机构: Kempelen Institute of Intelligent Technologies; University of Copenhagen (哥本哈根大学); Comenius University in Bratislava (布拉迪斯拉发夸美纽斯大学)
关键词: large language models, human-written texts rises, high-quality content indistinguishable, recent large language, generate high-quality content
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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Abstract:The capabilities of recent large language models (LLMs) to generate high-quality content indistinguishable by humans from human-written texts rises many concerns regarding their misuse. Previous research has shown that LLMs can be effectively misused for generating disinformation news articles following predefined narratives. Their capabilities to generate personalized (in various aspects) content have also been evaluated and mostly found usable. However, a combination of personalization and disinformation abilities of LLMs has not been comprehensively studied yet. Such a dangerous combination should trigger integrated safety filters of the LLMs, if there are some. This study fills this gap by evaluation of vulnerabilities of recent open and closed LLMs, and their willingness to generate personalized disinformation news articles in English. We further explore whether the LLMs can reliably meta-evaluate the personalization quality and whether the personalization affects the generated-texts detectability. Our results demonstrate the need for stronger safety-filters and disclaimers, as those are not properly functioning in most of the evaluated LLMs. Additionally, our study revealed that the personalization actually reduces the safety-filter activations; thus effectively functioning as a jailbreak. Such behavior must be urgently addressed by LLM developers and service providers.
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[NLP-45] Smarter Better Faster Longer: A Modern Bidirectional Encoder for Fast Memory Efficient and Long Context Finetuning and Inference

【速读】：该论文试图解决现有编码器模型（如BERT）在性能和效率方面的改进问题。解决方案的关键在于引入ModernBERT，通过现代模型优化技术，在保持高效能的同时显著提升模型性能。ModernBERT在2万亿个标记上进行训练，支持8192的序列长度，展示了在多种分类任务和单/多向量检索任务中的最先进结果，同时具备更高的速度和内存效率，适用于常见的GPU推理环境。

链接: https://arxiv.org/abs/2412.13663
作者: Benjamin Warner,Antoine Chaffin,Benjamin Clavié,Orion Weller,Oskar Hallström,Said Taghadouini,Alexis Gallagher,Raja Biswas,Faisal Ladhak,Tom Aarsen,Nathan Cooper,Griffin Adams,Jeremy Howard,Iacopo Poli
机构: Answer.AI; LightOn; Johns Hopkins University; NVIDIA; HuggingFace
关键词: great performance-size tradeoff, larger decoder-only models, BERT offer, Encoder-only transformer models, offer a great
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:Encoder-only transformer models such as BERT offer a great performance-size tradeoff for retrieval and classification tasks with respect to larger decoder-only models. Despite being the workhorse of numerous production pipelines, there have been limited Pareto improvements to BERT since its release. In this paper, we introduce ModernBERT, bringing modern model optimizations to encoder-only models and representing a major Pareto improvement over older encoders. Trained on 2 trillion tokens with a native 8192 sequence length, ModernBERT models exhibit state-of-the-art results on a large pool of evaluations encompassing diverse classification tasks and both single and multi-vector retrieval on different domains (including code). In addition to strong downstream performance, ModernBERT is also the most speed and memory efficient encoder and is designed for inference on common GPUs.
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[NLP-46] PsyDT: Using LLM s to Construct the Digital Twin of Psychological Counselor with Personalized Counseling Style for Psychological Counseling

【速读】：该论文试图解决现有心理健康领域的大型语言模型 (LLMs) 未能考虑心理咨询师个性化风格（包括语言风格和治疗技术等）的问题，导致无法满足寻求不同咨询风格的客户需求。解决方案的关键在于提出了一种名为 PsyDT 的新框架，通过动态单次学习 (dynamic one-shot learning) 和 GPT-4 捕捉特定咨询师的独特风格，并利用现有单轮长文本对话生成多轮对话，最终通过在合成数据集 PsyDTCorpus 上微调 LLMs，实现具有个性化咨询风格的心理咨询师数字孪生。该方法相较于传统依赖大量真实案例的构建方式，更为快速和经济。

链接: https://arxiv.org/abs/2412.13660
作者: Haojie Xie,Yirong Chen,Xiaofen Xing,Jingkai Lin,Xiangmin Xu
机构: South China University of Technology(华南理工大学); Pazhou Lab(琶洲实验室)
关键词: made significant progress, large language models, Digital Twin, counseling style, Psychological counselor
类目: Computation and Language (cs.CL)
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Abstract:Currently, large language models (LLMs) have made significant progress in the field of psychological counseling. However, existing mental health LLMs overlook a critical issue where they do not consider the fact that different psychological counselors exhibit different personal styles, including linguistic style and therapy techniques, etc. As a result, these LLMs fail to satisfy the individual needs of clients who seek different counseling styles. To help bridge this gap, we propose PsyDT, a novel framework using LLMs to construct the Digital Twin of Psychological counselor with personalized counseling style. Compared to the time-consuming and costly approach of collecting a large number of real-world counseling cases to create a specific counselor’s digital twin, our framework offers a faster and more cost-effective solution. To construct PsyDT, we utilize dynamic one-shot learning by using GPT-4 to capture counselor’s unique counseling style, mainly focusing on linguistic style and therapy techniques. Subsequently, using existing single-turn long-text dialogues with client’s questions, GPT-4 is guided to synthesize multi-turn dialogues of specific counselor. Finally, we fine-tune the LLMs on the synthetic dataset, PsyDTCorpus, to achieve the digital twin of psychological counselor with personalized counseling style. Experimental results indicate that our proposed PsyDT framework can synthesize multi-turn dialogues that closely resemble real-world counseling cases and demonstrate better performance compared to other baselines, thereby show that our framework can effectively construct the digital twin of psychological counselor with a specific counseling style.
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[NLP-47] SCOPE: Optimizing Key-Value Cache Compression in Long-context Generation

【速读】：该论文试图解决长上下文生成任务中Key-Value (KV) 缓存成为瓶颈的问题，特别是在解码阶段优化不足的情况。解决方案的关键在于提出了一个名为SCOPE的框架，该框架分别在预填充和解码阶段对KV缓存进行优化。具体来说，预填充阶段的KV缓存被保留以维持关键信息，而在解码阶段，提出了一种基于滑动窗口的新策略来选择必要的heavy hitters，并通过自适应和非连续策略进一步优化内存使用和传输。

链接: https://arxiv.org/abs/2412.13649
作者: Jialong Wu,Zhenglin Wang,Linhai Zhang,Yilong Lai,Yulan He,Deyu Zhou
机构: 未知
关键词: bottleneck of LLMs, LLMs for long-context, decoding phase, prefill phase, long-context generation
类目: Computation and Language (cs.CL)
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Abstract:Key-Value (KV) cache has become a bottleneck of LLMs for long-context generation. Despite the numerous efforts in this area, the optimization for the decoding phase is generally ignored. However, we believe such optimization is crucial, especially for long-output generation tasks based on the following two observations: (i) Excessive compression during the prefill phase, which requires specific full context impairs the comprehension of the reasoning task; (ii) Deviation of heavy hitters occurs in the reasoning tasks with long outputs. Therefore, SCOPE, a simple yet efficient framework that separately performs KV cache optimization during the prefill and decoding phases, is introduced. Specifically, the KV cache during the prefill phase is preserved to maintain the essential information, while a novel strategy based on sliding is proposed to select essential heavy hitters for the decoding phase. Memory usage and memory transfer are further optimized using adaptive and discontinuous strategies. Extensive experiments on LongGenBench show the effectiveness and generalization of SCOPE and its compatibility as a plug-in to other prefill-only KV compression methods.
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[NLP-48] G-VEval: A Versatile Metric for Evaluating Image and Video Captions Using GPT-4o

【速读】：该论文试图解决视觉描述生成（visual captioning）评估指标中存在的不足，特别是传统指标（如BLEU、METEOR、CIDEr、ROUGE）在语义深度上的缺失，以及现有训练指标（如CLIP-Score、PAC-S、Polos）在零样本场景中的局限性。解决方案的关键在于引入了一种名为G-VEval的新型评估指标，该指标基于GPT-4o，并结合了链式思维推理（chain-of-thought reasoning）在大规模多模态模型中的应用。G-VEval支持三种模式：无参考、仅参考和结合模式，适用于视频和图像输入，并提出了新的MSVD-Eval数据集，通过引入准确性（Accuracy）、完整性（Completeness）、简洁性（Conciseness）和相关性（Relevance）四个维度（ACCR），为评估提供了更透明和一致的框架。实验结果表明，G-VEval在人类注释的相关性上优于现有方法，为多样化的描述生成任务提供了灵活的解决方案。

链接: https://arxiv.org/abs/2412.13647
作者: Tony Cheng Tong,Sirui He,Zhiwen Shao,Dit-Yan Yeung
机构: 未知
关键词: Language Model-based metrics, Advanced Language Model-based, metrics, visual captioning, ROUGE often miss
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:Evaluation metric of visual captioning is important yet not thoroughly explored. Traditional metrics like BLEU, METEOR, CIDEr, and ROUGE often miss semantic depth, while trained metrics such as CLIP-Score, PAC-S, and Polos are limited in zero-shot scenarios. Advanced Language Model-based metrics also struggle with aligning to nuanced human preferences. To address these issues, we introduce G-VEval, a novel metric inspired by G-Eval and powered by the new GPT-4o. G-VEval uses chain-of-thought reasoning in large multimodal models and supports three modes: reference-free, reference-only, and combined, accommodating both video and image inputs. We also propose MSVD-Eval, a new dataset for video captioning evaluation, to establish a more transparent and consistent framework for both human experts and evaluation metrics. It is designed to address the lack of clear criteria in existing datasets by introducing distinct dimensions of Accuracy, Completeness, Conciseness, and Relevance (ACCR). Extensive results show that G-VEval outperforms existing methods in correlation with human annotations, as measured by Kendall tau-b and Kendall tau-c. This provides a flexible solution for diverse captioning tasks and suggests a straightforward yet effective approach for large language models to understand video content, paving the way for advancements in automated captioning. Codes are available at this https URL
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[NLP-49] On the Role of Model Prior in Real-World Inductive Reasoning

【速读】：该论文试图解决的问题是：在大型语言模型 (LLMs) 的假设生成过程中，模型先验 (model priors) 与上下文示例 (in-context demonstrations) 各自的作用及其相对重要性尚未得到充分探索。解决方案的关键在于通过系统性评估三种归纳推理策略在五个真实世界任务中的表现，揭示了假设生成主要由模型的固有先验驱动，而移除上下文示例对假设质量和下游使用的影响极小。进一步分析表明，即使在标签配置不同或标签反转的情况下，模型先验也难以被覆盖，从而强调了在实际归纳推理任务中更好地利用模型先验的潜力。

链接: https://arxiv.org/abs/2412.13645
作者: Zhuo Liu,Ding Yu,Hangfeng He
机构: University of Rochester (罗切斯特大学)
关键词: Large Language Models, Large Language, Language Models, generate hypotheses, generalize effectively
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:Large Language Models (LLMs) show impressive inductive reasoning capabilities, enabling them to generate hypotheses that could generalize effectively to new instances when guided by in-context demonstrations. However, in real-world applications, LLMs’ hypothesis generation is not solely determined by these demonstrations but is significantly shaped by task-specific model priors. Despite their critical influence, the distinct contributions of model priors versus demonstrations to hypothesis generation have been underexplored. This study bridges this gap by systematically evaluating three inductive reasoning strategies across five real-world tasks with three LLMs. Our empirical findings reveal that, hypothesis generation is primarily driven by the model’s inherent priors; removing demonstrations results in minimal loss of hypothesis quality and downstream usage. Further analysis shows the result is consistent across various label formats with different label configurations, and prior is hard to override, even under flipped labeling. These insights advance our understanding of the dynamics of hypothesis generation in LLMs and highlight the potential for better utilizing model priors in real-world inductive reasoning tasks.
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[NLP-50] Mind Your Theory: Theory of Mind Goes Deeper Than Reasoning

【速读】：该论文试图解决的问题是当前人工智能（AI）领域在理论心灵（Theory of Mind, ToM）能力评估中的局限性，特别是过度关注静态逻辑问题而忽视了动态环境中的实际应用。解决方案的关键在于提出一种改进的评估方法，借鉴认知任务中的动态环境，以更全面地评估大语言模型（LLMs）的ToM能力。具体来说，论文强调需要同时考虑两个步骤：1) 确定是否需要调用ToM，包括适当的思维深度（Depth of Mentalizing, DoM）；2) 在给定DoM的情况下应用正确的推理。当前的研究主要集中在第二步，而论文建议通过动态环境来评估这两步的综合表现，从而更准确地反映LLMs的ToM能力。

链接: https://arxiv.org/abs/2412.13631
作者: Eitan Wagner,Nitay Alon,Joseph M. Barnby,Omri Abend
机构: Hebrew University of Jerusalem(耶路撒冷希伯来大学); MPI for Biological Cybernetics(马克斯·普朗克生物控制论研究所); Royal Holloway University of London(伦敦大学皇家霍洛威学院)
关键词: Theory of Mind, object of investigation, central object, Depth of Mentalizing, ToM
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:Theory of Mind (ToM) capabilities in LLMs have recently become a central object of investigation. Cognitive science distinguishes between two steps required for ToM tasks: 1) determine whether to invoke ToM, which includes the appropriate Depth of Mentalizing (DoM), or level of recursion required to complete a task; and 2) applying the correct inference given the DoM. In this position paper, we first identify several lines of work in different communities in AI, including LLM benchmarking, ToM add-ons, ToM probing, and formal models for ToM. We argue that recent work in AI tends to focus exclusively on the second step which are typically framed as static logic problems. We conclude with suggestions for improved evaluation of ToM capabilities inspired by dynamic environments used in cognitive tasks.
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[NLP-51] LIFT: Improving Long Context Understanding Through Long Input Fine-Tuning

【速读】：该论文试图解决大语言模型（LLM）在长上下文理解方面的挑战，主要由于其有限的上下文窗口。解决方案的关键是提出了长输入微调框架（Long Input Fine-Tuning, LIFT），该框架通过在测试时调整模型参数以适应上下文，从而增强模型在长上下文任务中的表现。LIFT能够在不增加离线长上下文适应的计算负担的情况下，有效处理长输入，并提升任意短上下文模型的长上下文能力。此外，LIFT结合了上下文学习和预LIFT监督微调，进一步增强了框架的性能，使得像Llama 3这样的短上下文模型能够处理任意长度的上下文，并在LooGLE和LongBench等长上下文基准测试中持续提升表现。

链接: https://arxiv.org/abs/2412.13626
作者: Yansheng Mao,Jiaqi Li,Fanxu Meng,Jing Xiong,Zilong Zheng,Muhan Zhang
机构: Institute for Artificial Intelligence, Peking University(北京大学人工智能研究所); National Key Laboratory of General Artificial Intelligence, BIGAI(通用人工智能国家重点实验室, BIGAI)
关键词: limited context windows, large language models, language models due, understanding remains challenging, remains challenging
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:Long context understanding remains challenging for large language models due to their limited context windows. This paper introduces Long Input Fine-Tuning (LIFT) for long context modeling, a novel framework that enhances LLM performance on long-context tasks by adapting model parameters to the context at test time. LIFT enables efficient processing of lengthy inputs without the computational burden of offline long-context adaptation, and can improve the long-context capabilities of arbitrary short-context models. The framework is further enhanced by integrating in-context learning and pre-LIFT supervised fine-tuning. The combination of in-context learning and LIFT enables short-context models like Llama 3 to handle arbitrarily long contexts and consistently improves their performance on popular long-context benchmarks like LooGLE and LongBench. We also provide a comprehensive analysis of the strengths and limitations of LIFT on long context understanding, offering valuable directions for future research.
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[NLP-52] Reverse Region-to-Entity Annotation for Pixel-Level Visual Entity Linking AAAI2025

【速读】：该论文试图解决视觉实体链接 (Visual Entity Linking, VEL) 任务中复杂场景下文本输入不便的问题，提出了一种新的任务——像素级视觉实体链接 (Pixel-Level Visual Entity Linking, PL-VEL)，通过像素掩码 (pixel masks) 来指代图像中的对象，从而提供了一种更便捷的视觉输入方式。解决方案的关键在于构建了一个自动化的反向区域-实体标注框架，生成了包含超过500万条像素级区域与实体标签对齐的MaskOVEN-Wiki数据集，并通过视觉语义分词 (visual semantic tokenization) 方法增强了区域交互注意力机制，从而提升了模型的细粒度视觉理解能力。实验结果表明，该数据集使模型准确率提升了18个百分点，而语义分词方法进一步提升了5个百分点。

链接: https://arxiv.org/abs/2412.13614
作者: Zhengfei Xu,Sijia Zhao,Yanchao Hao,Xiaolong Liu,Lili Li,Yuyang Yin,Bo Li,Xi Chen,Xin Xin
机构: Beijing Institute of Technology(北京理工大学); Harbin Institute of Technology(哈尔滨工业大学)
关键词: Visual Entity Linking, Entity Linking, knowledge base, Visual Entity, Visual
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR); Multimedia (cs.MM)
备注: AAAI 2025;Dataset are released at this https URL

点击查看摘要

Abstract:Visual Entity Linking (VEL) is a crucial task for achieving fine-grained visual understanding, matching objects within images (visual mentions) to entities in a knowledge base. Previous VEL tasks rely on textual inputs, but writing queries for complex scenes can be challenging. Visual inputs like clicks or bounding boxes offer a more convenient alternative. Therefore, we propose a new task, Pixel-Level Visual Entity Linking (PL-VEL), which uses pixel masks from visual inputs to refer to objects, supplementing reference methods for VEL. To facilitate research on this task, we have constructed the MaskOVEN-Wiki dataset through an entirely automatic reverse region-entity annotation framework. This dataset contains over 5 million annotations aligning pixel-level regions with entity-level labels, which will advance visual understanding towards fine-grained. Moreover, as pixel masks correspond to semantic regions in an image, we enhance previous patch-interacted attention with region-interacted attention by a visual semantic tokenization approach. Manual evaluation results indicate that the reverse annotation framework achieved a 94.8% annotation success rate. Experimental results show that models trained on this dataset improved accuracy by 18 points compared to zero-shot models. Additionally, the semantic tokenization method achieved a 5-point accuracy improvement over the trained baseline.
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[NLP-53] Are LLM s Good Literature Review Writers? Evaluating the Literature Review Writing Ability of Large Language Models

【速读】：该论文试图解决大语言模型（LLMs）在撰写全面文献综述方面的实际能力问题，特别是它们是否能够生成准确和可靠的参考文献。解决方案的关键在于提出一个自动化评估框架，用于评估LLMs在生成参考文献、撰写摘要和撰写文献综述三个任务中的表现。该框架通过外部工具进行多维度的评估，包括参考文献中的幻觉率（hallucination rates）、语义覆盖（semantic coverage）和与人类撰写内容的事实一致性（factual consistency）。实验结果表明，尽管模型有所进步，但仍无法完全避免生成幻觉参考文献，且不同模型在不同学科的文献综述写作中表现出不同的性能。

链接: https://arxiv.org/abs/2412.13612
作者: Xuemei Tang,Xufeng Duan,Zhenguang G. Cai
机构: The Chinese University of Hong Kong (香港中文大学); The Chinese University of Hong Kong (香港中文大学)
关键词: involves complex processes, crucial form, form of academic, involves complex, literature
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 12 pages, 7 figures, 5 tables

点击查看摘要

Abstract:The literature review is a crucial form of academic writing that involves complex processes of literature collection, organization, and summarization. The emergence of large language models (LLMs) has introduced promising tools to automate these processes. However, their actual capabilities in writing comprehensive literature reviews remain underexplored, such as whether they can generate accurate and reliable references. To address this gap, we propose a framework to assess the literature review writing ability of LLMs automatically. We evaluate the performance of LLMs across three tasks: generating references, writing abstracts, and writing literature reviews. We employ external tools for a multidimensional evaluation, which includes assessing hallucination rates in references, semantic coverage, and factual consistency with human-written context. By analyzing the experimental results, we find that, despite advancements, even the most sophisticated models still cannot avoid generating hallucinated references. Additionally, different models exhibit varying performance in literature review writing across different disciplines.
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[NLP-54] Beyond Outcomes: Transparent Assessment of LLM Reasoning in Games

【速读】：该论文试图解决当前大型语言模型（LLMs）在复杂推理任务中评估的挑战，包括解释性不足、性能饱和和数据污染等问题。解决方案的关键在于引入GAMEBoT，一个专门设计用于严格和透明评估LLM推理能力的游戏竞技场。GAMEBoT通过将复杂推理任务分解为预定义的模块化子问题，并设计一系列基于思维链（Chain-of-Thought, CoT）的提示，利用领域知识引导LLM解决这些子问题，从而实现对推理过程的细致评估。此外，论文还开发了一套基于规则的算法来生成子问题的真实答案，确保对LLM中间推理步骤的严格验证。这种方法不仅评估最终行动的质量，还验证了推理过程的准确性，并通过动态游戏和LLM之间的竞争自然地缓解了数据污染的风险。

链接: https://arxiv.org/abs/2412.13602
作者: Wenye Lin,Jonathan Roberts,Yunhan Yang,Samuel Albanie,Zongqing Lu,Kai Han
机构: 未知
关键词: Large Language Models, Large Language, Language Models, increasingly deployed, deployed in real-world
类目: Computation and Language (cs.CL)
备注: 8 pages

点击查看摘要

Abstract:Large Language Models (LLMs) are increasingly deployed in real-world applications that demand complex reasoning. To track progress, robust benchmarks are required to evaluate their capabilities beyond superficial pattern recognition. However, current LLM reasoning benchmarks often face challenges such as insufficient interpretability, performance saturation or data contamination. To address these challenges, we introduce GAMEBoT, a gaming arena designed for rigorous and transparent assessment of LLM reasoning capabilities. GAMEBoT decomposes complex reasoning in games into predefined modular subproblems. This decomposition allows us to design a suite of Chain-of-Thought (CoT) prompts that leverage domain knowledge to guide LLMs in addressing these subproblems before action selection. Furthermore, we develop a suite of rule-based algorithms to generate ground truth for these subproblems, enabling rigorous validation of the LLMs’ intermediate reasoning steps. This approach facilitates evaluation of both the quality of final actions and the accuracy of the underlying reasoning process. GAMEBoT also naturally alleviates the risk of data contamination through dynamic games and head-to-head LLM competitions. We benchmark 17 prominent LLMs across eight games, encompassing various strategic abilities and game characteristics. Our results suggest that GAMEBoT presents a significant challenge, even when LLMs are provided with detailed CoT prompts. Project page: \urlthis https URL
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[NLP-55] Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation

【速读】：该论文试图解决胸部X射线（CXR）中的解剖异常检测与报告生成这两个任务之间的独立性问题，提出了一种协同进化异常检测与报告生成（CoE-DG）框架。解决方案的关键在于利用全标注（带有边界框注释和临床报告）和弱标注（仅有报告）数据，通过双向信息交互策略实现两个任务的相互促进。具体来说，引入了生成器引导信息传播（GIP）和检测器引导信息传播（DIP）机制，前者通过生成器提取的信息辅助检测器，并利用生成器的预测来优化检测器的伪标签；后者则利用检测器预测的异常类别和位置来指导生成器生成更准确的报告。此外，还提出了自适应非极大值抑制模块（SA-NMS），用于动态校正检测器生成的伪检测标签，从而提升报告生成的质量。

链接: https://arxiv.org/abs/2412.13599
作者: Jinghan Sun,Dong Wei,Zhe Xu,Donghuan Lu,Hong Liu,Hong Wang,Sotirios A. Tsaftaris,Steven McDonagh,Yefeng Zheng,Liansheng Wang
机构: National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China; Jarvis Research Center, Tencent YouTu Lab; Medical Artificial Intelligence Laboratory, Westlake University, Hangzhou, China; Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China; School of Engineering, The University of Edinburgh, Edinburgh, UK
关键词: Anatomical abnormality detection, chest X-ray, Anatomical abnormality, report generation, abnormality detection
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:

点击查看摘要

Abstract:Anatomical abnormality detection and report generation of chest X-ray (CXR) are two essential tasks in clinical practice. The former aims at localizing and characterizing cardiopulmonary radiological findings in CXRs, while the latter summarizes the findings in a detailed report for further diagnosis and treatment. Existing methods often focused on either task separately, ignoring their correlation. This work proposes a co-evolutionary abnormality detection and report generation (CoE-DG) framework. The framework utilizes both fully labeled (with bounding box annotations and clinical reports) and weakly labeled (with reports only) data to achieve mutual promotion between the abnormality detection and report generation tasks. Specifically, we introduce a bi-directional information interaction strategy with generator-guided information propagation (GIP) and detector-guided information propagation (DIP). For semi-supervised abnormality detection, GIP takes the informative feature extracted by the generator as an auxiliary input to the detector and uses the generator’s prediction to refine the detector’s pseudo labels. We further propose an intra-image-modal self-adaptive non-maximum suppression module (SA-NMS). This module dynamically rectifies pseudo detection labels generated by the teacher detection model with high-confidence predictions by the this http URL, for report generation, DIP takes the abnormalities’ categories and locations predicted by the detector as input and guidance for the generator to improve the generated reports.
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[NLP-56] EvoWiki: Evaluating LLM s on Evolving Knowledge

【速读】：该论文试图解决现有基准测试主要为静态，无法准确捕捉大型语言模型（LLMs）和知识演变的动态特性，导致评估不准确和存在污染等漏洞的问题。解决方案的关键在于引入EvoWiki，一个能够反映知识演变的动态数据集，通过将信息分类为稳定、演变和未知状态，并实现全自动更新，从而精确评估不断变化的知识和新发布的LLMs。EvoWiki通过实验验证了检索增强生成（RAG）和持续学习（CL）在适应演变知识方面的协同效应，为未来研究提供了强大的基准。

链接: https://arxiv.org/abs/2412.13582
作者: Wei Tang,Yixin Cao,Yang Deng,Jiahao Ying,Bo Wang,Yizhe Yang,Yuyue Zhao,Qi Zhang,Xuanjing Huang,Yugang Jiang,Yong Liao
机构: University of Science and Technology of China; CCCD Key Lab of Ministry of Culture and Tourism; School of Computer Science, Fudan University; Singapore Management University; Beijing Institute of Technology
关键词: effective deployment, critical aspect, Knowledge, evolving knowledge, evolving
类目: Computation and Language (cs.CL)
备注:

点击查看摘要

Abstract:Knowledge utilization is a critical aspect of LLMs, and understanding how they adapt to evolving knowledge is essential for their effective deployment. However, existing benchmarks are predominantly static, failing to capture the evolving nature of LLMs and knowledge, leading to inaccuracies and vulnerabilities such as contamination. In this paper, we introduce EvoWiki, an evolving dataset designed to reflect knowledge evolution by categorizing information into stable, evolved, and uncharted states. EvoWiki is fully auto-updatable, enabling precise evaluation of continuously changing knowledge and newly released LLMs. Through experiments with Retrieval-Augmented Generation (RAG) and Contunual Learning (CL), we evaluate how effectively LLMs adapt to evolving knowledge. Our results indicate that current models often struggle with evolved knowledge, frequently providing outdated or incorrect responses. Moreover, the dataset highlights a synergistic effect between RAG and CL, demonstrating their potential to better adapt to evolving knowledge. EvoWiki provides a robust benchmark for advancing future research on the knowledge evolution capabilities of large language models.
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[NLP-57] Socio-Culturally Aware Evaluation Framework for LLM -Based Content Moderation COLING2025

【速读】：该论文试图解决现有内容审核数据集在不同群体代表性不足的问题，导致评估结果不可靠。解决方案的关键在于提出一个社会文化意识评估框架，并引入基于角色生成 (persona-based generation) 的可扩展方法来创建多样化的数据集。这种方法相较于仅关注多样性的生成方法，能够提供更广泛的视角，并为较小规模的语言模型 (LLMs) 带来更大的挑战，从而凸显其在处理多样化内容时的困难。

链接: https://arxiv.org/abs/2412.13578
作者: Shanu Kumar,Gauri Kholkar,Saish Mendke,Anubhav Sadana,Parag Agrawal,Sandipan Dandapat
机构: Microsoft Corporation(微软公司); Pure Storage; Relay42
关键词: large language models, language models, growth of social, social media, media and large
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted in SUMEval Workshop in COLING 2025

点击查看摘要

Abstract:With the growth of social media and large language models, content moderation has become crucial. Many existing datasets lack adequate representation of different groups, resulting in unreliable assessments. To tackle this, we propose a socio-culturally aware evaluation framework for LLM-driven content moderation and introduce a scalable method for creating diverse datasets using persona-based generation. Our analysis reveals that these datasets provide broader perspectives and pose greater challenges for LLMs than diversity-focused generation methods without personas. This challenge is especially pronounced in smaller LLMs, emphasizing the difficulties they encounter in moderating such diverse content.
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[NLP-58] Generating Long-form Story Using Dynamic Hierarchical Outlining with Memory-Enhancement

【速读】：该论文试图解决长篇故事生成任务中存在的上下文一致性和情节连贯性问题。现有方法（包括大型语言模型）通常依赖于固定的提纲或缺乏宏观层面的规划，导致在长篇故事生成中难以同时实现上下文一致性和情节的连贯发展。解决方案的关键在于提出了动态分层提纲与记忆增强的长篇故事生成方法（Dynamic Hierarchical Outlining with Memory-Enhancement, DOME）。具体来说，动态分层提纲机制（Dynamic Hierarchical Outline, DHO）将小说写作理论融入提纲规划，并将规划与写作阶段融合，确保情节的完整性并适应故事生成过程中的不确定性，从而提升情节的连贯性。同时，基于时间知识图谱的记忆增强模块（Memory-Enhancement Module, MEM）用于存储和访问生成的内容，减少上下文冲突，进一步提高故事的连贯性。最后，通过时间冲突分析器（Temporal Conflict Analyzer）自动评估长篇故事的上下文一致性。实验结果表明，DOME在流畅性、连贯性和整体质量上显著优于现有最先进的方法。

链接: https://arxiv.org/abs/2412.13575
作者: Qianyue Wang,Jinwu Hu,Zhengping Li,Yufeng Wang,daiyuan li,Yu Hu,Mingkui Tan
机构: South China University of Technology; Pazhou Laboratory; Peng Cheng Laboratory; Hong Kong Polytechnic University
关键词: sufficiently lengthy text, writingand interactive storytelling, Long-form story generation, generation task aims, Long-form story
类目: Computation and Language (cs.CL)
备注: 39 pages

点击查看摘要

Abstract:Long-form story generation task aims to produce coherent and sufficiently lengthy text, essential for applications such as novel writingand interactive storytelling. However, existing methods, including LLMs, rely on rigid outlines or lack macro-level planning, making it difficult to achieve both contextual consistency and coherent plot development in long-form story generation. To address this issues, we propose Dynamic Hierarchical Outlining with Memory-Enhancement long-form story generation method, named DOME, to generate the long-form story with coherent content and plot. Specifically, the Dynamic Hierarchical Outline(DHO) mechanism incorporates the novel writing theory into outline planning and fuses the plan and writing stages together, improving the coherence of the plot by ensuring the plot completeness and adapting to the uncertainty during story generation. A Memory-Enhancement Module (MEM) based on temporal knowledge graphs is introduced to store and access the generated content, reducing contextual conflicts and improving story coherence. Finally, we propose a Temporal Conflict Analyzer leveraging temporal knowledge graphs to automatically evaluate the contextual consistency of long-form story. Experiments demonstrate that DOME significantly improves the fluency, coherence, and overall quality of generated long stories compared to state-of-the-art methods.
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[NLP-59] EscapeBench: Pushing Language Models to Think Outside the Box

【速读】：该论文试图解决现有语言模型在陌生环境中缺乏创造性适应能力的问题。解决方案的关键在于引入EscapeBench基准测试，通过设计复杂的密室逃脱游戏环境，挑战模型在创意推理、非常规工具使用和迭代问题解决方面的能力，以揭示隐含目标。论文提出的EscapeAgent框架通过Foresight（创新工具使用）和Reflection（识别未解决任务）来增强创造性推理，实验结果表明该框架在执行长链动作时保持逻辑一致性，并显著减少了完成任务所需的步骤和提示，提升了在不同难度级别下的表现和解谜效率。

链接: https://arxiv.org/abs/2412.13549
作者: Cheng Qian,Peixuan Han,Qinyu Luo,Bingxiang He,Xiusi Chen,Yuji Zhang,Hongyi Du,Jiarui Yao,Xiaocheng Yang,Denghui Zhang,Yunzhu Li,Heng Ji
机构: University of Illinois Urbana-Champaign; Johns Hopkins University; Stevens Institute of Technology; Columbia University
关键词: Language model agents, neglecting creative adaptation, existing benchmarks primarily, benchmarks primarily focus, model agents excel
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 23 pages, 15 figures

点击查看摘要

Abstract:Language model agents excel in long-session planning and reasoning, but existing benchmarks primarily focus on goal-oriented tasks with explicit objectives, neglecting creative adaptation in unfamiliar environments. To address this, we introduce EscapeBench, a benchmark suite of room escape game environments designed to challenge agents with creative reasoning, unconventional tool use, and iterative problem-solving to uncover implicit goals. Our results show that current LM models, despite employing working memory and Chain-of-Thought reasoning, achieve only 15% average progress without hints, highlighting their limitations in creativity. To bridge this gap, we propose EscapeAgent, a framework designed to enhance creative reasoning through Foresight (innovative tool use) and Reflection (identifying unsolved tasks). Experiments show that EscapeAgent can execute action chains over 1,000 steps while maintaining logical coherence. It navigates and completes games with up to 40% fewer steps and hints, performs robustly across varying difficulty levels, and achieves higher action success rates with more efficient and innovative puzzle-solving strategies. All the data and codes are released.
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[NLP-60] Query-centric Audio-Visual Cognition Network for Moment Retrieval Segmentation and Step-Captioning AAAI2025

【速读】：该论文试图解决在视频检索、时刻检索、时刻分割和步骤字幕生成等任务中，现有方法难以全面理解用户偏好的内容的问题。关键解决方案是提出了一种以查询为中心的视听认知网络（QUAG），通过浅层到深层的认知原则，构建可靠的多模态表示。具体而言，QUAG首先通过模态协同感知（modality-synergistic perception）建模视觉和音频模态之间的全局对比对齐和局部细粒度交互，以获取丰富的视听内容。随后，利用深层查询进行时间-通道过滤，对浅层视听表示进行处理，从而实现对用户偏好内容的认知，并生成适用于三个任务的以查询为中心的视听表示。实验结果表明，QUAG在HIREST任务中达到了最先进（SOTA）的性能，并在基于查询的视频摘要任务中验证了其良好的泛化能力。

链接: https://arxiv.org/abs/2412.13543
作者: Yunbin Tu,Liang Li,Li Su,Qingming Huang
机构: 1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China(哈尔滨工业大学计算机科学与技术学院，哈尔滨，中国);
2. School of Computer Science and Engineering, Beihang University, Beijing, China(北京航空航天大学计算机科学与工程学院，北京，中国)
关键词: favored multimedia format, including video retrieval, video retrieval, favored multimedia, multimedia format
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted by AAAI 2025

点击查看摘要

Abstract:Video has emerged as a favored multimedia format on the internet. To better gain video contents, a new topic HIREST is presented, including video retrieval, moment retrieval, moment segmentation, and step-captioning. The pioneering work chooses the pre-trained CLIP-based model for video retrieval, and leverages it as a feature extractor for other three challenging tasks solved in a multi-task learning paradigm. Nevertheless, this work struggles to learn the comprehensive cognition of user-preferred content, due to disregarding the hierarchies and association relations across modalities. In this paper, guided by the shallow-to-deep principle, we propose a query-centric audio-visual cognition (QUAG) network to construct a reliable multi-modal representation for moment retrieval, segmentation and step-captioning. Specifically, we first design the modality-synergistic perception to obtain rich audio-visual content, by modeling global contrastive alignment and local fine-grained interaction between visual and audio modalities. Then, we devise the query-centric cognition that uses the deep-level query to perform the temporal-channel filtration on the shallow-level audio-visual representation. This can cognize user-preferred content and thus attain a query-centric audio-visual representation for three tasks. Extensive experiments show QUAG achieves the SOTA results on HIREST. Further, we test QUAG on the query-based video summarization task and verify its good generalization.
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[NLP-61] Multi-Granularity Open Intent Classification via Adaptive Granular-Ball Decision Boundary AAAI2025

【速读】：该论文试图解决开放意图分类中的问题，即在对话系统中准确分类已知意图并识别未知意图。传统基于边界的方法假设已知意图分布在紧凑的球形区域内，并依赖粗粒度表示和精确的球形决策边界，但在实际场景中这些假设往往不成立，导致难以区分已知意图和未知意图。论文提出的解决方案是多粒度开放意图分类方法（MOGB），其关键在于通过自适应粒球决策边界来解决上述问题。MOGB方法包括表示学习和决策边界获取两个模块，采用层次化表示学习方法，通过迭代自适应粒球聚类和最近子质心分类来捕捉已知意图类中的细粒度语义结构，并构建多粒度决策边界，使用具有不同质心和半径的粒球来实现开放意图分类。

链接: https://arxiv.org/abs/2412.13542
作者: Yanhua Li,Xiaocao Ouyang,Chaofan Pan,Jie Zhang,Sen Zhao,Shuyin Xia,Xin Yang,Guoyin Wang,Tianrui Li
机构: 1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China(哈尔滨工业大学计算机科学与技术学院，哈尔滨，中国);
2. Key Laboratory of Network Security and Cryptology, State Key Laboratory of Information Security, Chinese Academy of Sciences, Beijing, China(网络安全与密码学重点实验室，信息安全国家重点实验室，中国科学院，北京，中国);
3. School of Computer Science and Technology, Tianjin University, Tianjin, China(天津大学计算机科学与技术学院，天津，中国);
4. School of Computer Science and Technology, Shandong University, Jinan, China(山东大学计算机科学与技术学院，济南，中国);
5. School of Computer Science and Technology, Southwest Jiaotong University, Chengdu, China(西南交通大学计算机科学与技术学院，成都，中国)
关键词: Open intent classification, Open intent, dialogue systems, aiming to accurately, identifying unknown intents
类目: Computation and Language (cs.CL)
备注: This paper has been Accepted on AAAI2025

点击查看摘要

Abstract:Open intent classification is critical for the development of dialogue systems, aiming to accurately classify known intents into their corresponding classes while identifying unknown intents. Prior boundary-based methods assumed known intents fit within compact spherical regions, focusing on coarse-grained representation and precise spherical decision boundaries. However, these assumptions are often violated in practical scenarios, making it difficult to distinguish known intent classes from unknowns using a single spherical boundary. To tackle these issues, we propose a Multi-granularity Open intent classification method via adaptive Granular-Ball decision boundary (MOGB). Our MOGB method consists of two modules: representation learning and decision boundary acquiring. To effectively represent the intent distribution, we design a hierarchical representation learning method. This involves iteratively alternating between adaptive granular-ball clustering and nearest sub-centroid classification to capture fine-grained semantic structures within known intent classes. Furthermore, multi-granularity decision boundaries are constructed for open intent classification by employing granular-balls with varying centroids and radii. Extensive experiments conducted on three public datasets demonstrate the effectiveness of our proposed method.
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[NLP-62] Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and Reasoning

【速读】：该论文试图解决大视觉-语言模型 (Large Vision-Language Models, LVLMs) 在处理视觉图结构时表现出的显著局限性问题。解决方案的关键在于提出了一个结构感知的微调框架 (structure-aware fine-tuning framework)，通过三个自监督学习任务来增强 LVLMs 的结构学习能力。实验验证了该方法在提升 LVLMs 在基础图学习任务上的零样本性能以及增强其对复杂视觉图的鲁棒性方面的有效性。

链接: https://arxiv.org/abs/2412.13540
作者: Yingjie Zhu,Xuefeng Bai,Kehai Chen,Yang Xiang,Min Zhang
机构: Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen, China(计算与智能研究所，哈尔滨工业大学深圳校区，中国); Peng Cheng Laboratory, Shenzhen, China(鹏城实验室，深圳，中国)
关键词: Large Vision-Language Models, Large Vision-Language, Vision-Language Models, demonstrated remarkable performance, demonstrated remarkable
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注:

点击查看摘要

Abstract:Large Vision-Language Models (LVLMs) have demonstrated remarkable performance across diverse tasks. Despite great success, recent studies show that LVLMs encounter substantial limitations when engaging with visual graphs. To study the reason behind these limitations, we propose VGCure, a comprehensive benchmark covering 22 tasks for examining the fundamental graph understanding and reasoning capacities of LVLMs. Extensive evaluations conducted on 14 LVLMs reveal that LVLMs are weak in basic graph understanding and reasoning tasks, particularly those concerning relational or structurally complex information. Based on this observation, we propose a structure-aware fine-tuning framework to enhance LVLMs with structure learning abilities through 3 self-supervised learning tasks. Experiments validate the effectiveness of our method in improving LVLMs’ zero-shot performance on fundamental graph learning tasks, as well as enhancing the robustness of LVLMs against complex visual graphs.
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[NLP-63] MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules

【速读】：该论文试图解决大语言模型在数学推理中的局限性，特别是其无法捕捉底层逻辑的问题。解决方案的关键在于提出了一种基于Transformer架构的新模型MetaRuleGPT，该模型不仅学习任务特定的知识，还通过元学习（meta-learning）获取可迁移的问题解决技能。MetaRuleGPT通过预训练在包含基本、复合和迭代规则的抽象数据集上，能够精确执行数值计算和复杂逻辑操作，从而模仿人类的规则遵循能力，分解复杂性，并迭代推导出复杂数学问题的准确结果。

链接: https://arxiv.org/abs/2412.13536
作者: Kejie Chen,Lin Wang,Qinghai Zhang,Renjun Xu
机构: Zhejiang University (浙江大学)
关键词: Recent studies, underlying logic, studies have highlighted, highlighted the limitations, limitations of large
类目: Computation and Language (cs.CL)
备注: 8 pages, 6 figures

点击查看摘要

Abstract:Recent studies have highlighted the limitations of large language models in mathematical reasoning, particularly their inability to capture the underlying logic. Inspired by meta-learning, we propose that models should acquire not only task-specific knowledge but also transferable problem-solving skills. We introduce MetaRuleGPT, a novel Transformer-based architecture that performs precise numerical calculations and complex logical operations by learning and combining different rules. In contrast with traditional training sets, which are heavily composed of massive raw instance data, MetaRuleGPT is pre-trained on much less abstract datasets containing basic, compound, and iterative rules for mathematical reasoning. Extensive experimental results demonstrate MetaRuleGPT can mimic human’s rule-following capabilities, break down complexity, and iteratively derive accurate results for complex mathematical problems. These findings prove the potential of rule learning to enhance the numerical reasoning abilities of language models.
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[NLP-64] Information-Theoretic Generative Clustering of Documents AAAI2025

【速读】：该论文试图解决文档聚类问题，提出了一种基于生成式聚类 (Generative Clustering, GC) 的方法，通过使用大型语言模型 (LLMs) 生成的文本 Y 来替代原始文档 X 进行聚类。解决方案的关键在于利用 LLMs 提供的概率分布，通过信息论中的 KL 散度来严格定义文档间的相似性，并提出了一种基于重要性采样的新型聚类算法。该方法不仅在性能上达到了最先进的水平，显著优于以往的聚类方法，还在生成式文档检索应用中通过层次聚类提高了检索准确性。

链接: https://arxiv.org/abs/2412.13534
作者: Xin Du,Kumiko Tanaka-Ishii
机构: 未知
关键词: mathrm, large language models, language models, clustering, original documents
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Information Retrieval (cs.IR); Information Theory (cs.IT)
备注: Accepted to AAAI 2025

点击查看摘要

Abstract:We present \em generative clustering (GC) for clustering a set of documents, \mathrmX , by using texts \mathrmY generated by large language models (LLMs) instead of by clustering the original documents \mathrmX . Because LLMs provide probability distributions, the similarity between two documents can be rigorously defined in an information-theoretic manner by the KL divergence. We also propose a natural, novel clustering algorithm by using importance sampling. We show that GC achieves the state-of-the-art performance, outperforming any previous clustering method often by a large margin. Furthermore, we show an application to generative document retrieval in which documents are indexed via hierarchical clustering and our method improves the retrieval accuracy.
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[NLP-65] CEHA: A Dataset of Conflict Events in the Horn of Africa COLING2025

【速读】：该论文试图解决在非洲之角地区暴力冲突事件的细粒度分类问题，现有数据集未能涵盖所有相关事件类型。解决方案的关键在于引入了一个新的基准数据集——非洲之角地区冲突事件数据集 (Conflict Events in the Horn of Africa region, CEHA)，并提出了基于该数据集的暴力冲突事件识别任务。该数据集包含500条英文事件描述，强调冲突原因的细粒度事件类型定义，并根据人道主义-和平-发展联结 (Humanitarian-Peace-Development Nexus) 中利益相关者的需求对关键冲突风险类型进行分类。论文还通过事件相关性分类和事件类型分类两个任务展示了数据集的挑战性和在低资源环境下的模型评估价值。

链接: https://arxiv.org/abs/2412.13511
作者: Rui Bai,Di Lu,Shihao Ran,Elizabeth Olson,Hemank Lamba,Aoife Cahill,Joel Tetreault,Alex Jaimes
机构: Dataminr Inc.
关键词: Natural Language Processing, Natural Language, Language Processing, Horn of Africa, conflict events
类目: Computation and Language (cs.CL)
备注: Accepted by COLING 2025

点击查看摘要

Abstract:Natural Language Processing (NLP) of news articles can play an important role in understanding the dynamics and causes of violent conflict. Despite the availability of datasets categorizing various conflict events, the existing labels often do not cover all of the fine-grained violent conflict event types relevant to areas like the Horn of Africa. In this paper, we introduce a new benchmark dataset Conflict Events in the Horn of Africa region (CEHA) and propose a new task for identifying violent conflict events using online resources with this dataset. The dataset consists of 500 English event descriptions regarding conflict events in the Horn of Africa region with fine-grained event-type definitions that emphasize the cause of the conflict. This dataset categorizes the key types of conflict risk according to specific areas required by stakeholders in the Humanitarian-Peace-Development Nexus. Additionally, we conduct extensive experiments on two tasks supported by this dataset: Event-relevance Classification and Event-type Classification. Our baseline models demonstrate the challenging nature of these tasks and the usefulness of our dataset for model evaluations in low-resource settings with limited number of training data.
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[NLP-66] Dynamic Adapter with Semantics Disentangling for Cross-lingual Cross-modal Retrieval AAAI-25 AAAI

【速读】：该论文试图解决在低资源语言环境下进行跨语言跨模态检索（Cross-lingual Cross-modal Retrieval, CCR）的问题，即在不使用任何人工标注的目标语言数据的情况下，实现视觉与低资源语言的对齐。解决方案的关键在于提出了动态适配器（Dynamic Adapter with Semantics Disentangling, DASD），其参数根据输入文本的特征动态生成。具体来说，通过语义解耦模块（semantic disentangling module）提取输入文本的语义相关特征和语义无关特征，确保生成的适配器能够适应输入文本的多样表达，从而提高跨语言跨模态检索的效果。

链接: https://arxiv.org/abs/2412.13510
作者: Rui Cai,Zhiyu Dong,Jianfeng Dong,Xun Wang
机构: 未知
关键词: Existing cross-modal retrieval, methods typically rely, retrieval methods typically, cross-modal retrieval methods, Cross-lingual Cross-modal Retrieval
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注: Accepted by the 39th AAAI Conference on Artificial Intelligence (AAAI-25)

点击查看摘要

Abstract:Existing cross-modal retrieval methods typically rely on large-scale vision-language pair data. This makes it challenging to efficiently develop a cross-modal retrieval model for under-resourced languages of interest. Therefore, Cross-lingual Cross-modal Retrieval (CCR), which aims to align vision and the low-resource language (the target language) without using any human-labeled target-language data, has gained increasing attention. As a general parameter-efficient way, a common solution is to utilize adapter modules to transfer the vision-language alignment ability of Vision-Language Pretraining (VLP) models from a source language to a target language. However, these adapters are usually static once learned, making it difficult to adapt to target-language captions with varied expressions. To alleviate it, we propose Dynamic Adapter with Semantics Disentangling (DASD), whose parameters are dynamically generated conditioned on the characteristics of the input captions. Considering that the semantics and expression styles of the input caption largely influence how to encode it, we propose a semantic disentangling module to extract the semantic-related and semantic-agnostic features from the input, ensuring that generated adapters are well-suited to the characteristics of input caption. Extensive experiments on two image-text datasets and one video-text dataset demonstrate the effectiveness of our model for cross-lingual cross-modal retrieval, as well as its good compatibility with various VLP models.
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[NLP-67] VaeDiff-DocRE: End-to-end Data Augmentation Framework for Document-level Relation Extraction COLING2025

【速读】：该论文试图解决文档级关系抽取 (Document-level Relation Extraction, DocRE) 中由于标签分布不均导致的性能下降问题。解决方案的关键在于提出了一种基于生成模型的数据增强方法，利用变分自编码器 (Variational Autoencoder, VAE) 捕捉实体对表示形成的所有关系分布，并通过扩散模型对VAE的潜在空间进行参数化，以增强数据中代表性不足的关系。此外，论文还引入了一个分层训练框架，将VAE增强模块集成到DocRE系统中，从而有效应对长尾分布问题，并在基准数据集上取得了优于现有最先进模型的性能。

链接: https://arxiv.org/abs/2412.13503
作者: Khai Phan Tran,Wen Hua,Xue Li
机构: School of Electrical Engineering and Computer Science, The University of Queensland, Australia; Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong SAR, China
关键词: Document-level Relation Extraction, Document-level Relation, Relation Extraction, aims to identify, identify relationships
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: COLING 2025

点击查看摘要

Abstract:Document-level Relation Extraction (DocRE) aims to identify relationships between entity pairs within a document. However, most existing methods assume a uniform label distribution, resulting in suboptimal performance on real-world, imbalanced datasets. To tackle this challenge, we propose a novel data augmentation approach using generative models to enhance data from the embedding space. Our method leverages the Variational Autoencoder (VAE) architecture to capture all relation-wise distributions formed by entity pair representations and augment data for underrepresented relations. To better capture the multi-label nature of DocRE, we parameterize the VAE’s latent space with a Diffusion Model. Additionally, we introduce a hierarchical training framework to integrate the proposed VAE-based augmentation module into DocRE systems. Experiments on two benchmark datasets demonstrate that our method outperforms state-of-the-art models, effectively addressing the long-tail distribution problem in DocRE.
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[NLP-68] Refining Salience-Aware Sparse Fine-Tuning Strategies for Language Models

【速读】：该论文试图解决参数高效微调 (Parameter-Efficient Fine-Tuning, PEFT) 中的灵活性和效率问题，特别是通过稀疏性基础的 PEFT (Sparsity-based PEFT, SPEFT) 方法。解决方案的关键在于引入可训练的稀疏适应性到模型权重矩阵中，并系统评估了显著性度量指标，发现基于梯度的简单度量指标在计算效率和性能上均表现出色。此外，论文比较了静态和动态掩码策略，发现静态掩码在预先确定非零项后进行训练，既提高了效率又不牺牲性能，而动态掩码并未带来显著优势。最终，基于梯度的静态度量 SPEFT 在自然语言处理任务中持续优于其他微调方法，为 SPEFT 提供了一个简单而有效的基线。

链接: https://arxiv.org/abs/2412.13488
作者: Xinxin Liu,Aaron Thomas,Cheng Zhang,Jianyi Cheng,Yiren Zhao,Xitong Gao
机构: Southern University of Science and Technology, China (南方科技大学); Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China (中国科学院深圳先进技术研究院); University of Birmingham, UK (伯明翰大学); Imperial College London, UK (伦敦帝国学院); University of Edinburgh, UK (爱丁堡大学); Shenzhen University of Advanced Technology, China (深圳先进技术大学)
关键词: gained prominence, PEFT, low-rank adaptation methods, SPEFT, sparsity-based PEFT
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:Parameter-Efficient Fine-Tuning (PEFT) has gained prominence through low-rank adaptation methods like LoRA. In this paper, we focus on sparsity-based PEFT (SPEFT), which introduces trainable sparse adaptations to the weight matrices in the model, offering greater flexibility in selecting fine-tuned parameters compared to low-rank methods. We conduct the first systematic evaluation of salience metrics for SPEFT, inspired by zero-cost NAS proxies, and identify simple gradient-based metrics is reliable, and results are on par with the best alternatives, offering both computational efficiency and robust performance. Additionally, we compare static and dynamic masking strategies, finding that static masking, which predetermines non-zero entries before training, delivers efficiency without sacrificing performance, while dynamic masking offers no substantial benefits. Across NLP tasks, a simple gradient-based, static SPEFT consistently outperforms other fine-tuning methods for LLMs, providing a simple yet effective baseline for SPEFT. Our work challenges the notion that complexity is necessary for effective PEFT. Our work is open source and available to the community at [this https URL].
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[NLP-69] 3-S2S: Training-free Triplet Tuning for Sketch to Scene Generation

【速读】：该论文试图解决生成式 AI 在复杂场景生成中，尤其是包含多个详细对象的场景中，容易遗漏小或不常见实例的问题。解决方案的关键在于提出了一种无需训练的三重调优方法（Training-free Triplet Tuning for Sketch-to-Scene, T3-S2S），通过重新审视交叉注意力机制，改进了现有的 ControlNet 模型。具体来说，该方法包括三个核心模块：提示平衡模块（prompt balance module）用于增强关键词表示，减少遗漏关键实例的风险；特征突出模块（characteristics prominence module）通过突出每个通道中的 TopK 索引，确保重要特征的更好表示；密集调优（dense tuning）用于细化注意力图中的轮廓细节，补偿实例相关区域。实验证明，该方法显著提升了现有草图到图像模型的性能，能够生成详细的多实例 2D 图像，并更好地遵循输入提示，提升复杂多实例场景的视觉质量。

链接: https://arxiv.org/abs/2412.13486
作者: Zhenhong Sun,Yifu Wang,Yonhon Ng,Yunfei Duan,Daoyi Dong,Hongdong Li,Pan Ji
机构: Australian National University(澳大利亚国立大学); XR Vision Labs, Tencent(XR Vision Labs, 腾讯)
关键词: computer graphics applications, graphics applications, computer graphics, Training-free Triplet Tuning, scene concept art
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Graphics (cs.GR)
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点击查看摘要

Abstract:Scene generation is crucial to many computer graphics applications. Recent advances in generative AI have streamlined sketch-to-image workflows, easing the workload for artists and designers in creating scene concept art. However, these methods often struggle for complex scenes with multiple detailed objects, sometimes missing small or uncommon instances. In this paper, we propose a Training-free Triplet Tuning for Sketch-to-Scene (T3-S2S) generation after reviewing the entire cross-attention mechanism. This scheme revitalizes the existing ControlNet model, enabling effective handling of multi-instance generations, involving prompt balance, characteristics prominence, and dense tuning. Specifically, this approach enhances keyword representation via the prompt balance module, reducing the risk of missing critical instances. It also includes a characteristics prominence module that highlights TopK indices in each channel, ensuring essential features are better represented based on token sketches. Additionally, it employs dense tuning to refine contour details in the attention map, compensating for instance-related regions. Experiments validate that our triplet tuning approach substantially improves the performance of existing sketch-to-image models. It consistently generates detailed, multi-instance 2D images, closely adhering to the input prompts and enhancing visual quality in complex multi-instance scenes. Code is available at this https URL.
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[NLP-70] Curriculum Learning for Cross-Lingual Data-to-Text Generation With Noisy Data

【速读】：该论文试图解决跨语言数据到文本生成 (cross-lingual data-to-text generation, DTG) 系统在处理噪声数据时的性能问题。解决方案的关键在于使用课程学习 (curriculum learning) 策略，通过特定的难度标准对训练样本进行排序，并结合退火调度 (annealing schedule) 来训练模型。具体而言，论文采用了对齐分数 (alignment score) 作为排序标准，并在两个不同的数据集上验证了该方法的有效性，结果显示BLEU分数提升了最多4分，同时生成的文本在忠实度和覆盖率方面平均提高了5-15%。

链接: https://arxiv.org/abs/2412.13484
作者: Kancharla Aditya Hari,Manish Gupta,Vasudeva Varma
机构: Sentisum; Microsoft, Hyderabad, India; IIIT Hyderabad
关键词: text generation systems, training samples, improve the quality, quality of text, Curriculum learning
类目: Computation and Language (cs.CL)
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Abstract:Curriculum learning has been used to improve the quality of text generation systems by ordering the training samples according to a particular schedule in various tasks. In the context of data-to-text generation (DTG), previous studies used various difficulty criteria to order the training samples for monolingual DTG. These criteria, however, do not generalize to the crosslingual variant of the problem and do not account for noisy data. We explore multiple criteria that can be used for improving the performance of cross-lingual DTG systems with noisy data using two curriculum schedules. Using the alignment score criterion for ordering samples and an annealing schedule to train the model, we show increase in BLEU score by up to 4 points, and improvements in faithfulness and coverage of generations by 5-15% on average across 11 Indian languages and English in 2 separate datasets. We make code and data publicly available
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[NLP-71] A Statistical and Multi-Perspective Revisiting of the Membership Inference Attack in Large Language Models

【速读】：该论文试图解决大型语言模型（LLMs）中数据透明性不足导致的成员推理攻击（Membership Inference Attack, MIA）性能不一致的问题。解决方案的关键在于通过在多种设置下进行数千次实验，统计性地重新审视MIA方法，并研究文本特征、嵌入、阈值决策以及成员和非成员的解码动态。研究发现，MIA性能随模型规模增加而提升，且在不同领域表现不同；尽管整体性能较低，但存在显著的成员和非成员可区分性异常值；阈值决策是一个被忽视的挑战；文本差异性和长文本有助于提升MIA性能；成员和非成员在LLM嵌入中表现出不同的解码动态。

链接: https://arxiv.org/abs/2412.13475
作者: Bowen Chen,Namgi Han,Yusuke Miyao
机构: The University of Tokyo (东京大学)
关键词: Membership Inference Attack, Large Language Models, Inference Attack, Large Language, Membership Inference
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: main content 8 pages, 6 figures

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Abstract:The lack of data transparency in Large Language Models (LLMs) has highlighted the importance of Membership Inference Attack (MIA), which differentiates trained (member) and untrained (non-member) data. Though it shows success in previous studies, recent research reported a near-random performance in different settings, highlighting a significant performance inconsistency. We assume that a single setting doesn’t represent the distribution of the vast corpora, causing members and non-members with different distributions to be sampled and causing inconsistency. In this study, instead of a single setting, we statistically revisit MIA methods from various settings with thousands of experiments for each MIA method, along with study in text feature, embedding, threshold decision, and decoding dynamics of members and non-members. We found that (1) MIA performance improves with model size and varies with domains, while most methods do not statistically outperform baselines, (2) Though MIA performance is generally low, a notable amount of differentiable member and non-member outliers exists and vary across MIA methods, (3) Deciding a threshold to separate members and non-members is an overlooked challenge, (4) Text dissimilarity and long text benefit MIA performance, (5) Differentiable or not is reflected in the LLM embedding, (6) Member and non-members show different decoding dynamics.
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[NLP-72] Gradual Vigilance and Interval Communication: Enhancing Value Alignment in Multi-Agent Debates

【速读】：该论文试图解决大语言模型在训练数据中可能引入有害内容的问题，强调了价值对齐的必要性。主流方法依赖于反馈学习和监督训练，资源消耗大且可能限制模型的潜力。论文提出的解决方案是基于多智能体辩论 (Multi-Agent Debate, MAD) 的框架，称为渐进警觉与间隔通信 (Gradual Vigilance and Interval Communication, GVIC)。该框架通过智能体之间的交互生成可靠答案，允许智能体在不同警觉水平下评估风险，并通过间隔通信交换多样化信息，从而优化辩论效率并减少通信开销。实验结果表明，GVIC在有害性缓解和欺诈预防方面显著优于基线方法，并展现出对不同基础模型大小和任务类型的强大适应性。

链接: https://arxiv.org/abs/2412.13471
作者: Rui Zou,Mengqi Wei,Jintian Feng,Qian Wan,Jianwen Sun,Sannyuya Liu
机构: 未知
关键词: shown exceptional performance, large language models, fulfilling diverse human, recent years, large language
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:In recent years, large language models have shown exceptional performance in fulfilling diverse human needs. However, their training data can introduce harmful content, underscoring the necessity for robust value alignment. Mainstream methods, which depend on feedback learning and supervised training, are resource-intensive and may constrain the full potential of the models. Multi-Agent Debate (MAD) offers a more efficient and innovative solution by enabling the generation of reliable answers through agent interactions. To apply MAD to value alignment, we examine the relationship between the helpfulness and harmlessness of debate outcomes and individual responses, and propose a MAD based framework Gradual Vigilance and Interval Communication (GVIC). GVIC allows agents to assess risks with varying levels of vigilance and to exchange diverse information through interval communication. We theoretically prove that GVIC optimizes debate efficiency while reducing communication overhead. Experimental results demonstrate that GVIC consistently outperforms baseline methods across various tasks and datasets, particularly excelling in harmfulness mitigation and fraud prevention. Additionally, GVIC exhibits strong adaptability across different base model sizes, including both unaligned and aligned models, and across various task types.
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[NLP-73] ransducer Tuning: Efficient Model Adaptation for Software Tasks Using Code Property Graphs

【速读】：该论文试图解决在资源受限环境下，由于大规模语言模型（Large Language Models）的可训练参数增加而导致的内存需求过高问题。解决方案的关键在于引入了一种名为\approach的技术，通过使用代码属性图（Code Property Graphs, CPGs）来适应下游代码任务，而无需对模型进行全参数微调。具体来说，该方法的核心组件是一个称为\transducer的模块，它包括图向量化引擎（Graph Vectorization Engine, GVE）和基于注意力的融合层（Attention-Based Fusion Layer, ABFL）。GVE从输入源代码中提取CPGs并将其转换为图特征向量，ABFL则将这些图特征向量与大规模语言模型的初始代码嵌入进行融合。通过优化这些转换器以适应不同的下游任务，该方法在不增加可训练参数的情况下提升了模型性能，显著减少了内存需求，同时在与全参数微调和其它微调方法（如LoRA、Prompt-Tuning、Prefix-Tuning）的对比中表现出色。

链接: https://arxiv.org/abs/2412.13467
作者: Imam Nur Bani Yusuf,Lingxiao Jiang
机构: Singapore Management University (新加坡管理大学)
关键词: Large language, Large language models, software engineering tasks, demonstrated promising performance, Large
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Under review

点击查看摘要

Abstract:Large language models have demonstrated promising performance across various software engineering tasks. While fine-tuning is a common practice to adapt these models for downstream tasks, it becomes challenging in resource-constrained environments due to increased memory requirements from growing trainable parameters in increasingly large language models. We introduce \approach, a technique to adapt large models for downstream code tasks using Code Property Graphs (CPGs). Our approach introduces a modular component called \transducer that enriches code embeddings with structural and dependency information from CPGs. The Transducer comprises two key components: Graph Vectorization Engine (GVE) and Attention-Based Fusion Layer (ABFL). GVE extracts CPGs from input source code and transforms them into graph feature vectors. ABFL then fuses those graphs feature vectors with initial code embeddings from a large language model. By optimizing these transducers for different downstream tasks, our approach enhances the models without the need to fine-tune them for specific tasks. We have evaluated \approach on three downstream tasks: code summarization, assert generation, and code translation. Our results demonstrate competitive performance compared to full parameter fine-tuning while reducing up to 99% trainable parameters to save memory. \approach also remains competitive against other fine-tuning approaches (e.g., LoRA, Prompt-Tuning, Prefix-Tuning) while using only 1.5%-80% of their trainable parameters. Our findings show that integrating structural and dependency information through Transducer Tuning enables more efficient model adaptation, making it easier for users to adapt large models in resource-constrained settings.
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[NLP-74] GenX: Mastering Code and Test Generation with Execution Feedback

【速读】：该论文试图解决现有代码生成方法依赖于预先存在的测试用例（test cases）的问题，这些测试用例可能不总是可用或全面。解决方案的关键在于提出了一种新颖的方法，即同时训练代码生成模型和测试生成模型，并通过执行反馈（execution feedback）来优化两者的性能。具体策略包括测试和代码数据增强（data augmentation）以及新的评分函数（scoring function）用于代码和测试的排序。实验结果表明，通过迭代训练和增加测试用例及代码解决方案，该方法在APPS数据集上表现优于仅依赖原始数据集的模型。

链接: https://arxiv.org/abs/2412.13464
作者: Nan Wang,Yafei Liu,Chen Chen,Haonan Lu
机构: OPPO AI Center(OPPO人工智能中心)
关键词: Recent advancements, improve code generation, language modeling, natural language, modeling have enabled
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL)
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点击查看摘要

Abstract:Recent advancements in language modeling have enabled the translation of natural language into code, and the use of execution feedback to improve code generation. However, these methods often rely heavily on pre-existing test cases, which may not always be available or comprehensive. In this work, we propose a novel approach that concurrently trains a code generation model and a test generation model, utilizing execution feedback to refine and enhance the performance of both. We introduce two strategies for test and code data augmentation and a new scoring function for code and test ranking. We experiment on the APPS dataset and demonstrate that our approach can effectively generate and augment test cases, filter and synthesize correct code solutions, and rank the quality of generated code and tests. The results demonstrate that our models, when iteratively trained with an increasing number of test cases and code solutions, outperform those trained on the original dataset.
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[NLP-75] FlashVTG: Feature Layering and Adaptive Score Handling Network for Video Temporal Grounding WACV2025

【速读】：该论文试图解决文本引导的视频时序定位 (Text-guided Video Temporal Grounding, VTG) 中短视频片段检索 (Moment Retrieval, MR) 和亮点检测 (Highlight Detection, HD) 的挑战，主要问题在于传统方法依赖稀疏且有限的解码器查询，导致预测精度受限，并且忽视了视频的整体上下文信息。解决方案的关键在于引入FlashVTG框架，该框架包含时序特征分层模块 (Temporal Feature Layering, TFL) 和自适应分数优化模块 (Adaptive Score Refinement, ASR)。TFL模块通过替代传统解码器结构，捕捉多时序尺度上的视频内容变化；ASR模块则通过整合相邻时刻和多时序尺度特征来优化预测排序。实验表明，FlashVTG在多个数据集上实现了最先进的性能，显著提升了短时刻检索的准确性。

链接: https://arxiv.org/abs/2412.13441
作者: Zhuo Cao,Bingqing Zhang,Heming Du,Xin Yu,Xue Li,Sen Wang
机构: The University of Queensland, Australia(昆士兰大学, 澳大利亚)
关键词: Highlight Detection, localize relevant segments, Text-guided Video Temporal, Video Temporal Grounding, Temporal Grounding
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted to WACV 2025

点击查看摘要

Abstract:Text-guided Video Temporal Grounding (VTG) aims to localize relevant segments in untrimmed videos based on textual descriptions, encompassing two subtasks: Moment Retrieval (MR) and Highlight Detection (HD). Although previous typical methods have achieved commendable results, it is still challenging to retrieve short video moments. This is primarily due to the reliance on sparse and limited decoder queries, which significantly constrain the accuracy of predictions. Furthermore, suboptimal outcomes often arise because previous methods rank predictions based on isolated predictions, neglecting the broader video context. To tackle these issues, we introduce FlashVTG, a framework featuring a Temporal Feature Layering (TFL) module and an Adaptive Score Refinement (ASR) module. The TFL module replaces the traditional decoder structure to capture nuanced video content variations across multiple temporal scales, while the ASR module improves prediction ranking by integrating context from adjacent moments and multi-temporal-scale features. Extensive experiments demonstrate that FlashVTG achieves state-of-the-art performance on four widely adopted datasets in both MR and HD. Specifically, on the QVHighlights dataset, it boosts mAP by 5.8% for MR and 3.3% for HD. For short-moment retrieval, FlashVTG increases mAP to 125% of previous SOTA performance. All these improvements are made without adding training burdens, underscoring its effectiveness. Our code is available at this https URL.
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[NLP-76] Lightweight Safety Classification Using Pruned Language Models

【速读】：该论文试图解决大语言模型（LLM）在内容安全和提示注入分类方面的挑战。解决方案的关键在于提出了一种名为层增强分类（Layer Enhanced Classification, LEC）的新技术，该技术通过在LLM的最优中间转换层（optimal intermediate transformer layer）的隐藏状态上训练一个惩罚性逻辑回归（Penalized Logistic Regression, PLR）分类器，结合了PLR的高效计算和LLM的复杂语言理解能力，从而在性能上超越了GPT-4o和专门为每个任务微调的模型。研究表明，小型通用模型（如Qwen 2.5的0.5B、1.5B和3B版本）和其他基于Transformer的架构（如DeBERTa v3）作为鲁棒的特征提取器，能够在少于100个高质量样本上有效训练简单分类器。此外，这些模型的中间转换层通常在分类任务中优于最终层，表明单一通用LLM可以同时用于分类、检测提示注入和生成输出。

链接: https://arxiv.org/abs/2412.13435
作者: Mason Sawtell,Tula Masterman,Sandi Besen,Jim Brown
机构: Neudesic, an IBM Company; IBM
关键词: Penalized Logistic Regression, Large Language Models, Large Language, Logistic Regression, Penalized Logistic
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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点击查看摘要

Abstract:In this paper, we introduce a novel technique for content safety and prompt injection classification for Large Language Models. Our technique, Layer Enhanced Classification (LEC), trains a Penalized Logistic Regression (PLR) classifier on the hidden state of an LLM’s optimal intermediate transformer layer. By combining the computational efficiency of a streamlined PLR classifier with the sophisticated language understanding of an LLM, our approach delivers superior performance surpassing GPT-4o and special-purpose models fine-tuned for each task. We find that small general-purpose models (Qwen 2.5 sizes 0.5B, 1.5B, and 3B) and other transformer-based architectures like DeBERTa v3 are robust feature extractors allowing simple classifiers to be effectively trained on fewer than 100 high-quality examples. Importantly, the intermediate transformer layers of these models typically outperform the final layer across both classification tasks. Our results indicate that a single general-purpose LLM can be used to classify content safety, detect prompt injections, and simultaneously generate output tokens. Alternatively, these relatively small LLMs can be pruned to the optimal intermediate layer and used exclusively as robust feature extractors. Since our results are consistent on different transformer architectures, we infer that robust feature extraction is an inherent capability of most, if not all, LLMs.
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[NLP-77] Enhancing Talk Moves Analysis in Mathematics Tutoring through Classroom Teaching Discourse COLING’2025

【速读】：该论文试图解决在数学辅导对话中，如何高效地收集、标注和分析大量对话数据以开发机器学习模型的问题。解决方案的关键在于提出了一个名为SAGA22的紧凑数据集，并通过多种建模策略（包括对话上下文、说话者信息、预训练数据集和进一步微调）来提升模型性能。研究表明，利用课堂数据的预训练能够显著增强辅导场景中的模型表现，尤其是在结合更长上下文和说话者信息时。此外，论文通过广泛的消融研究强调了对话行为建模中的挑战。

链接: https://arxiv.org/abs/2412.13395
作者: Jie Cao,Abhijit Suresh,Jennifer Jacobs,Charis Clevenger,Amanda Howard,Chelsea Brown,Brent Milne,Tom Fischaber,Tamara Sumner,James H. Martin
机构: University of Oklahoma; Institute of Cognitive Science, University of Colorado Boulder; Saga Education
关键词: Human tutoring interventions, promoting personal growth, supporting student learning, improving academic performance, tutoring interventions play
类目: Computation and Language (cs.CL)
备注: Accepted to COLING’2025

点击查看摘要

Abstract:Human tutoring interventions play a crucial role in supporting student learning, improving academic performance, and promoting personal growth. This paper focuses on analyzing mathematics tutoring discourse using talk moves - a framework of dialogue acts grounded in Accountable Talk theory. However, scaling the collection, annotation, and analysis of extensive tutoring dialogues to develop machine learning models is a challenging and resource-intensive task. To address this, we present SAGA22, a compact dataset, and explore various modeling strategies, including dialogue context, speaker information, pretraining datasets, and further fine-tuning. By leveraging existing datasets and models designed for classroom teaching, our results demonstrate that supplementary pretraining on classroom data enhances model performance in tutoring settings, particularly when incorporating longer context and speaker information. Additionally, we conduct extensive ablation studies to underscore the challenges in talk move modeling.
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[NLP-78] Catalysts of Conversation: Examining Interaction Dynamics Between Topic Initiators and Commentors in Alzheimers Disease Online Communities

【速读】：该论文旨在解决非正式护理者（如家庭成员或朋友）在照顾阿尔茨海默病及相关痴呆症（ADRD）患者时，如何通过在线社区有效获取信息和情感支持的问题。研究的关键在于识别驱动这些在线社区用户互动的因素，特别是主题发起者的参与度、初始帖子的内容以及评论的语言模式。通过使用倾向评分匹配、主题建模和预测建模等分析方法，研究发现活跃的主题发起者参与度能显著增加评论数量，而主题发起者的互惠回复则能进一步促进社区层面的评论者参与。此外，实用性护理主题能促使主题发起者重新参与，而情感支持主题则吸引更多其他评论者的参与。评论的语言复杂性和情感基调也会影响其获得主题发起者回复的可能性。这些发现强调了促进积极和互惠的参与，以及提供有效策略以增强ADRD护理和更广泛的健康相关在线社区可持续性的重要性。

链接: https://arxiv.org/abs/2412.13388
作者: Congning Ni,Qingxia Chen,Lijun Song,Patricia Commiskey,Qingyuan Song,Bradley A. Malin,Zhijun Yin
机构: Vanderbilt University(范德堡大学); Vanderbilt University Medical Center(范德堡大学医学中心)
关键词: Related Dementias, Alzheimers Disease, Disease and Related, face substantial challenges, living with Alzheimers
类目: Computers and Society (cs.CY); Computation and Language (cs.CL); Machine Learning (cs.LG); Applications (stat.AP)
备注: 14 pages, 11 figures (6 in main text and 5 in the appendix). The paper includes statistical analyses, structural topic modeling, and predictive modeling to examine user engagement dynamics in Alzheimers Disease online communities. Submitted for consideration to The Web Conference 2025

点击查看摘要

Abstract:Informal caregivers (e.g.,family members or friends) of people living with Alzheimers Disease and Related Dementias (ADRD) face substantial challenges and often seek informational or emotional support through online communities. Understanding the factors that drive engagement within these platforms is crucial, as it can enhance their long-term value for caregivers by ensuring that these communities effectively meet their needs. This study investigated the user interaction dynamics within two large, popular ADRD communities, TalkingPoint and ALZConnected, focusing on topic initiator engagement, initial post content, and the linguistic patterns of comments at the thread level. Using analytical methods such as propensity score matching, topic modeling, and predictive modeling, we found that active topic initiator engagement drives higher comment volumes, and reciprocal replies from topic initiators encourage further commentor engagement at the community level. Practical caregiving topics prompt more re-engagement of topic initiators, while emotional support topics attract more comments from other commentors. Additionally, the linguistic complexity and emotional tone of a comment influence its likelihood of receiving replies from topic initiators. These findings highlight the importance of fostering active and reciprocal engagement and providing effective strategies to enhance sustainability in ADRD caregiving and broader health-related online communities.
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[NLP-79] An Automated Explainable Educational Assessment System Built on LLM s AAAI2025

【速读】：该论文试图解决自动化教育评估系统中解释性不足的问题，以及人工标注的高成本问题。解决方案的关键在于利用大型语言模型 (LLMs) 生成自动评分和解释性理由，并通过交互式和可视化的评估工具提供教育者和研究者对评估准确性和模型生成理由质量的洞察。此外，系统还提供了先进的可视化功能和强大的评估工具，以增强教育评估的可用性并促进理由验证的效率。

链接: https://arxiv.org/abs/2412.13381
作者: Jiazheng Li,Artem Bobrov,David West,Cesare Aloisi,Yulan He
机构: Lancaster University(兰卡斯特大学); University of Essex(埃塞克斯大学)
关键词: present AERA Chat, AERA Chat, present AERA, explainable educational assessment, assessment system designed
类目: Computation and Language (cs.CL)
备注: Accepted to AAAI 2025

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Abstract:In this demo, we present AERA Chat, an automated and explainable educational assessment system designed for interactive and visual evaluations of student responses. This system leverages large language models (LLMs) to generate automated marking and rationale explanations, addressing the challenge of limited explainability in automated educational assessment and the high costs associated with annotation. Our system allows users to input questions and student answers, providing educators and researchers with insights into assessment accuracy and the quality of LLM-assessed rationales. Additionally, it offers advanced visualization and robust evaluation tools, enhancing the usability for educational assessment and facilitating efficient rationale verification. Our demo video can be found at this https URL.
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[NLP-80] SummExecEdit: A Factual Consistency Benchmark in Summarization with Executable Edits

【速读】：该论文试图解决摘要生成中事实不一致性检测的挑战，现有基准测试缺乏足够的难度和可解释性，无法进行稳健评估。解决方案的关键在于引入SummExecEdit基准，通过可执行的编辑操作来评估模型在检测事实错误和提供准确解释方面的能力。该基准不仅能够检测错误，还能提供详细的解释，从而提高评估的全面性和准确性。

链接: https://arxiv.org/abs/2412.13378
作者: Onkar Thorat,Philippe Laban,Chien-Sheng Wu
机构: Salesforce AI Research(Salesforce AI研究)
关键词: Detecting factual inconsistencies, existing benchmarks lack, Detecting factual, summarization is critical, robust evaluation
类目: Computation and Language (cs.CL)
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点击查看摘要

Abstract:Detecting factual inconsistencies in summarization is critical, yet existing benchmarks lack the necessary challenge and interpretability for robust evaluation. In this paper, we introduce SummExecEdit, a novel benchmark leveraging executable edits to assess models on their ability to both detect factual errors and provide accurate explanations. The top-performing model, Claude3-Opus, achieves a joint detection and explanation score of only 0.49 in our benchmark, with individual scores of 0.67 for detection and 0.73 for explanation. Furthermore, we identify four primary types of explanation errors, with 45.4% of errors focusing on completely unrelated parts of the summary.
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[NLP-81] DateLogicQA: Benchmarking Temporal Biases in Large Language Models

【速读】：该论文试图解决时间推理（temporal reasoning）任务中的准确性问题，特别是针对不同日期格式、时间上下文和推理类型的复杂性。解决方案的关键在于提出了DateLogicQA基准，包含190个问题，用于全面评估大型语言模型（LLMs）在时间推理中的能力。此外，论文引入了语义完整性度量（Semantic Integrity Metric）来评估分词质量，并分析了两种偏差：表示层偏差（Representation-Level Bias）和逻辑层偏差（Logical-Level Bias），分别影响嵌入表示和推理输出。这些方法共同揭示了LLMs在处理时间数据时的关键挑战和局限性。

链接: https://arxiv.org/abs/2412.13377
作者: Gagan Bhatia,MingZe Tang,Cristina Mahanta,Madiha Kazi
机构: University of Aberdeen (阿伯丁大学)
关键词: paper introduces DateLogicQA, questions covering diverse, diverse date formats, covering diverse date, Semantic Integrity Metric
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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点击查看摘要

Abstract:This paper introduces DateLogicQA, a benchmark with 190 questions covering diverse date formats, temporal contexts, and reasoning types. We propose the Semantic Integrity Metric to assess tokenization quality and analyse two biases: Representation-Level Bias, affecting embeddings, and Logical-Level Bias, influencing reasoning outputs. Our findings provide a comprehensive evaluation of LLMs’ capabilities and limitations in temporal reasoning, highlighting key challenges in handling temporal data accurately. The GitHub repository for our work is available at this https URL
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[NLP-82] Extending LLM s to New Languages: A Case Study of Llama and Persian Adaptation COLING2025

【速读】：该论文试图解决大型语言模型（LLMs）在低资源语言（如波斯语）上的表现不佳问题。解决方案的关键在于采用参数高效的微调方法，通过多阶段训练策略来增强模型对波斯语的理解和表现。具体步骤包括：首先在单语波斯语数据上进行预训练，然后通过双语预训练和指令数据集对齐表示，最后使用任务特定的数据集进行指令微调。研究结果表明，通过双语数据对齐可以提高波斯语任务的分类准确性，且对英语任务无负面影响，甚至在某些情况下有所提升。此外，研究还发现，模型在处理低资源语言时，其初始强度是关键因素，而跨语言对齐对低资源语言的提升效果有限。

链接: https://arxiv.org/abs/2412.13375
作者: Samin Mahdizadeh Sani,Pouya Sadeghi,Thuy-Trang Vu,Yadollah Yaghoobzadeh,Gholamreza Haffari
机构: University of Tehran(德黑兰大学); Tehran Institute for Advanced Studies(德黑兰高级研究所); Monash University(莫纳什大学)
关键词: made great progress, Large language models, Large language, made great, great progress
类目: Computation and Language (cs.CL)
备注: accepted at COLING 2025

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Abstract:Large language models (LLMs) have made great progress in classification and text generation tasks. However, they are mainly trained on English data and often struggle with low-resource languages. In this study, we explore adding a new language, i.e., Persian, to Llama (a model with a limited understanding of Persian) using parameter-efficient fine-tuning. We employ a multi-stage approach involving pretraining on monolingual Persian data, aligning representations through bilingual pretraining and instruction datasets, and instruction-tuning with task-specific datasets. We evaluate the model’s performance at each stage on generation and classification tasks. Our findings suggest that incorporating the Persian language, through bilingual data alignment, can enhance classification accuracy for Persian tasks, with no adverse impact and sometimes even improvements on English tasks. Additionally, the results highlight the model’s initial strength as a critical factor when working with limited training data, with cross-lingual alignment offering minimal benefits for the low-resource language. Knowledge transfer from English to Persian has a marginal effect, primarily benefiting simple classification tasks.
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[NLP-83] Experience of Training a 1.7B-Parameter LLaMa Model From Scratch

【速读】：该论文试图解决预训练大型语言模型（large language models）过程中涉及的多重复杂因素，包括模型架构、数据质量、训练连续性和硬件限制等问题。解决方案的关键在于通过精心策划的数据集（约200亿个token）和基于LLaMa架构的1.7亿参数模型（DMaS-LLaMa-Lite）进行训练，并详细记录了从初始的不连贯文本到流畅且上下文相关的输出的训练轨迹。论文强调了在恢复训练时恢复优化器状态（optimizer states）的重要性，以及硬件变化对训练稳定性和吞吐量的影响。此外，通过高质量数据和合理扩展策略，论文展示了在显著减少训练token数量的情况下，仍能获得具有竞争力的结果。

链接: https://arxiv.org/abs/2412.13335
作者: Miles Q. Li,Benjamin C. M. Fung,Shih-Chia Huang
机构: McGill University(麦吉尔大学); National Taipei University of Technology(台北科技大学)
关键词: complex endeavor influenced, including model architecture, Pretraining large language, large language models, multiple factors
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:Pretraining large language models is a complex endeavor influenced by multiple factors, including model architecture, data quality, training continuity, and hardware constraints. In this paper, we share insights gained from the experience of training DMaS-LLaMa-Lite, a fully open source, 1.7-billion-parameter, LLaMa-based model, on approximately 20 billion tokens of carefully curated data. We chronicle the full training trajectory, documenting how evolving validation loss levels and downstream benchmarks reflect transitions from incoherent text to fluent, contextually grounded output. Beyond standard quantitative metrics, we highlight practical considerations such as the importance of restoring optimizer states when resuming from checkpoints, and the impact of hardware changes on training stability and throughput. While qualitative evaluation provides an intuitive understanding of model improvements, our analysis extends to various performance benchmarks, demonstrating how high-quality data and thoughtful scaling enable competitive results with significantly fewer training tokens. By detailing these experiences and offering training logs, checkpoints, and sample outputs, we aim to guide future researchers and practitioners in refining their pretraining strategies. The training script is available on Github at this https URL. The model checkpoints are available on Huggingface at this https URL.
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[NLP-84] Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models

【速读】：该论文试图解决现有混合状态空间模型（State Space Models, SSMs）与注意力机制（Attention）结合时，无法有效回忆远距离历史信息的问题。解决方案的关键在于引入“扩展跨度注意力”（Span-Expanded Attention, SE-Attn）机制，通过为每个新的查询令牌动态分配相关性而非仅基于最近性的状态，从而允许模型“逐字”访问超出当前混合SSM注意力跨度的远距离令牌。具体实现上，论文提出了一种新的微调方法HyLoRA，通过扩展LoRA以适应混合模型，使得预训练的混合模型能够在比预训练时更长的令牌序列上高效适应。这一方法在处理具有长程依赖的自然语言任务时，表现出比现有方法如LongLoRA更高的性能和更低的成本。

链接: https://arxiv.org/abs/2412.13328
作者: Elvis Nunez,Luca Zancato,Benjamin Bowman,Aditya Golatkar,Wei Xia,Stefano Soatto
机构: UCLA; AWS AI Labs
关键词: State Space Models, State Space, Hybrid models, State Space layers, Hybrid
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:The “state” of State Space Models (SSMs) represents their memory, which fades exponentially over an unbounded span. By contrast, Attention-based models have “eidetic” (i.e., verbatim, or photographic) memory over a finite span (context size). Hybrid architectures combine State Space layers with Attention, but still cannot recall the distant past and can access only the most recent tokens eidetically. Unlike current methods of combining SSM and Attention layers, we allow the state to be allocated based on relevancy rather than recency. In this way, for every new set of query tokens, our models can “eidetically” access tokens from beyond the Attention span of current Hybrid SSMs without requiring extra hardware resources. We describe a method to expand the memory span of the hybrid state by “reserving” a fraction of the Attention context for tokens retrieved from arbitrarily distant in the past, thus expanding the eidetic memory span of the overall state. We call this reserved fraction of tokens the “expansion span,” and the mechanism to retrieve and aggregate it “Span-Expanded Attention” (SE-Attn). To adapt Hybrid models to using SE-Attn, we propose a novel fine-tuning method that extends LoRA to Hybrid models (HyLoRA) and allows efficient adaptation on long spans of tokens. We show that SE-Attn enables us to efficiently adapt pre-trained Hybrid models on sequences of tokens up to 8 times longer than the ones used for pre-training. We show that HyLoRA with SE-Attn is cheaper and more performant than alternatives like LongLoRA when applied to Hybrid models on natural language benchmarks with long-range dependencies, such as PG-19, RULER, and other common natural language downstream tasks.
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[NLP-85] Hint Marginalization for Improved Reasoning in Large Language Models

【速读】：该论文试图解决现有大型语言模型 (LLMs) 在推理任务中组合多个响应时效率低下的问题。解决方案的关键是提出了一个名为“Hint Marginalization”的新算法框架，该框架通过迭代采样策略形成蒙特卡洛近似，以识别最可能的答案分布的模式。这一方法在多个算术推理基准数据集上的实验结果显示了其优越性。

链接: https://arxiv.org/abs/2412.13292
作者: Soumyasundar Pal,Didier Chételat,Yingxue Zhang,Mark Coates
机构: Huawei Noah’s Ark Lab, Canada(华为诺亚方舟实验室，加拿大); McGill University(麦吉尔大学); Mila; ILLS
关键词: Large Language Models, Large Language, Language Models, perform reasoning tasks, intermediate steps
类目: Computation and Language (cs.CL)
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Abstract:Large Language Models (LLMs) have exhibited an impressive capability to perform reasoning tasks, especially if they are encouraged to generate a sequence of intermediate steps. Reasoning performance can be improved by suitably combining multiple LLM responses, generated either in parallel in a single query, or via sequential interactions with LLMs throughout the reasoning process. Existing strategies for combination, such as self-consistency and progressive-hint-prompting, make inefficient usage of the LLM responses. We present Hint Marginalization, a novel and principled algorithmic framework to enhance the reasoning capabilities of LLMs. Our approach can be viewed as an iterative sampling strategy for forming a Monte Carlo approximation of an underlying distribution of answers, with the goal of identifying the mode the most likely answer. Empirical evaluation on several benchmark datasets for arithmetic reasoning demonstrates the superiority of the proposed approach.
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[NLP-86] Enhancing Persona Classification in Dialogue Systems: A Graph Neural Network Approach

【速读】：该论文试图解决角色分类 (persona classification) 这一关键问题，这是对话理解中的重要组成部分，旨在通过将角色融入大型语言模型 (LLMs) 来提升对话的自然性和用户参与度。解决方案的关键在于提出了一种结合文本嵌入 (text embeddings) 和图神经网络 (Graph Neural Networks, GNNs) 的框架。具体来说，该框架通过提取角色陈述中的语义特征并构建一个图结构，其中节点代表角色，边表示角色间的相似性，利用 GNN 在图中传播相关信息，从而显著提升分类性能，尤其是在数据有限的情况下。此外，论文还创建了一个手动标注的角色分类数据集，以支持模型训练和评估。

链接: https://arxiv.org/abs/2412.13283
作者: Konstantin Zaitsev
机构: HSE / Moscow (HSE / 莫斯科)
关键词: Large Language Models, Large Language, gain considerable attention, enhance personalized experiences, Language Models
类目: Computation and Language (cs.CL)
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Abstract:In recent years, Large Language Models (LLMs) gain considerable attention for their potential to enhance personalized experiences in virtual assistants and chatbots. A key area of interest is the integration of personas into LLMs to improve dialogue naturalness and user engagement. This study addresses the challenge of persona classification, a crucial component in dialogue understanding, by proposing a framework that combines text embeddings with Graph Neural Networks (GNNs) for effective persona classification. Given the absence of dedicated persona classification datasets, we create a manually annotated dataset to facilitate model training and evaluation. Our method involves extracting semantic features from persona statements using text embeddings and constructing a graph where nodes represent personas and edges capture their similarities. The GNN component uses this graph structure to propagate relevant information, thereby improving classification performance. Experimental results show that our approach, in particular the integration of GNNs, significantly improves classification performance, especially with limited data. Our contributions include the development of a persona classification framework and the creation of a dataset.
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[NLP-87] In-Context Learning Distillation for Efficient Few-Shot Fine-Tuning

【速读】：该论文试图解决自然语言推理任务中模型参数过大和内存消耗过高的问题。解决方案的关键在于采用少样本上下文学习（few-shot in-context learning）结合知识蒸馏（knowledge distillation）技术，将OPT-1.3B模型的参数从1.3B减少到125M，模型大小从2.5GB缩减到0.25GB。这种方法不仅在域外准确率上比单纯使用上下文学习提升了近50%，还比传统的基于模式微调的方法减少了60%的内存消耗，同时提高了20%的域外准确率。

链接: https://arxiv.org/abs/2412.13243
作者: Yifei Duan,Liu Li,Zirui Zhai,Jinxia Yao
机构: Georgia Institute of Technology (佐治亚理工学院)
关键词: natural language inference, language inference task, applied few-shot in-context, reducing model parameter, few-shot in-context learning
类目: Computation and Language (cs.CL)
备注: 7 pages, 6 figures

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Abstract:We applied few-shot in-context learning on the OPT-1.3B model for the natural language inference task and employed knowledge distillation to internalize the context information, reducing model parameter from 1.3B to 125M and achieving a size reduction from 2.5GB to 0.25GB. Compared to using in-context learning alone on similarly sized models, this context distillation approach achieved a nearly 50% improvement in out-of-domain accuracy, demonstrating superior knowledge transfer capabilities over prompt-based methods. Furthermore, this approach reduced memory consumption by up to 60% while delivering a 20% improvement in out-of-domain accuracy compared to conventional pattern-based fine-tuning.
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[NLP-88] Adaptive Two-Phase Finetuning LLM s for Japanese Legal Text Retrieval

【速读】：该论文试图解决在日语文本检索（Text Retrieval）领域中，特别是法律文档检索方面的挑战。现有研究大多集中在英语领域，而针对日语环境的解决方案较少。论文的关键解决方案是提出了一种新颖的两阶段管道（two-phase pipeline），专门针对日语法律上下文进行优化。第一阶段，模型通过学习全局上下文来增强其泛化能力和对多样化查询的适应性；第二阶段，模型经过微调以处理法律场景中的复杂查询。实验结果表明，该方法在日语和英语环境中均表现出色，超越了现有的基线模型。

链接: https://arxiv.org/abs/2412.13205
作者: Quang Hoang Trung,Nguyen Van Hoang Phuc,Le Trung Hoang,Quang Huu Hieu,Vo Nguyen Le Duy
机构: VJ-Tech(VJ科技); AJ-Tech(AJ科技); UIT(UIT)
关键词: retrieving text-based content, text-based content relevant, Text Retrieval, involves finding, large repository
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:Text Retrieval (TR) involves finding and retrieving text-based content relevant to a user’s query from a large repository, with applications in real-world scenarios such as legal document retrieval. While most existing studies focus on English, limited work addresses Japanese contexts. In this paper, we introduce a new dataset specifically designed for Japanese legal contexts and propose a novel two-phase pipeline tailored to this domain. In the first phase, the model learns a broad understanding of global contexts, enhancing its generalization and adaptability to diverse queries. In the second phase, the model is fine-tuned to address complex queries specific to legal scenarios. Extensive experiments are conducted to demonstrate the superior performance of our method, which outperforms existing baselines. Furthermore, our pipeline proves effective in English contexts, surpassing comparable baselines on the MS MARCO dataset. We have made our code publicly available on GitHub, and the model checkpoints are accessible via HuggingFace. Subjects: Information Retrieval (cs.IR); Computation and Language (cs.CL); Machine Learning (cs.LG) Cite as: arXiv:2412.13205 [cs.IR] (or arXiv:2412.13205v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2412.13205 Focus to learn more arXiv-issued DOI via DataCite
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[NLP-89] Harnessing Transfer Learning from Swahili: Advancing Solutions for Comorian Dialects

【速读】：该论文试图解决非洲低资源语言（如科摩罗语）在自然语言处理（NLP）领域缺乏数据支持的问题。解决方案的关键在于采用迁移学习（Transfer Learning）策略，通过计算词汇距离，将斯瓦希里语中与科摩罗语最接近的数据元素用于构建混合数据集，从而提升科摩罗语在自动语音识别（ASR）和机器翻译（MT）任务中的表现。实验结果表明，机器翻译模型在ROUGE评分上取得了显著进展，而自动语音识别系统的词错误率（WER）和字符错误率（CER）也得到了有效控制。这一研究对于推动低资源语言的NLP技术发展具有重要意义。

链接: https://arxiv.org/abs/2412.12143
作者: Naira Abdou Mohamed,Zakarya Erraji,Abdessalam Bahafid,Imade Benelallam
机构: Institut National de Statistique et d’Economie Appliquée, Rabat, Morocco; ToumAI Analytics, Rabat, Morocco
关键词: Natural Language Processing, develop high-performing Natural, high-performing Natural Language, today some African, high-performing Natural
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: This paper was presented at the 6th Deep Learning Indaba Conference (DLI 2024)

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Abstract:If today some African languages like Swahili have enough resources to develop high-performing Natural Language Processing (NLP) systems, many other languages spoken on the continent are still lacking such support. For these languages, still in their infancy, several possibilities exist to address this critical lack of data. Among them is Transfer Learning, which allows low-resource languages to benefit from the good representation of other languages that are similar to them. In this work, we adopt a similar approach, aiming to pioneer NLP technologies for Comorian, a group of four languages or dialects belonging to the Bantu family. Our approach is initially motivated by the hypothesis that if a human can understand a different language from their native language with little or no effort, it would be entirely possible to model this process on a machine. To achieve this, we consider ways to construct Comorian datasets mixed with Swahili. One thing to note here is that in terms of Swahili data, we only focus on elements that are closest to Comorian by calculating lexical distances between candidate and source data. We empirically test this hypothesis in two use cases: Automatic Speech Recognition (ASR) and Machine Translation (MT). Our MT model achieved ROUGE-1, ROUGE-2, and ROUGE-L scores of 0.6826, 0.42, and 0.6532, respectively, while our ASR system recorded a WER of 39.50% and a CER of 13.76%. This research is crucial for advancing NLP in underrepresented languages, with potential to preserve and promote Comorian linguistic heritage in the digital age. Comments: This paper was presented at the 6th Deep Learning Indaba Conference (DLI 2024) Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2412.12143 [cs.CL] (or arXiv:2412.12143v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2412.12143 Focus to learn more arXiv-issued DOI via DataCite
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[NLP-90] Less is More: A Simple yet Effective Token Reduction Method for Efficient Multi-modal LLM s COLING2025

【速读】：该论文试图解决多模态大语言模型 (Multimodal Large Language Models, MLLMs) 在快速发展的同时带来的资源消耗激增问题。解决方案的关键是提出了一种名为“基于CLIP度量的Token缩减 (Token Reduction using CLIP Metric, TRIM)”的新方法，该方法通过模拟人类在视觉问答 (Visual Question Answering, VQA) 任务中的注意力模式，优化图像Token的选择与缩减，从而在不牺牲模型性能的前提下显著降低计算开销。TRIM方法在12个数据集上进行了广泛测试，结果表明其在保持一致性能的同时，大幅减少了计算资源的需求，推动了高效MLLM的发展，促进了高性能模型的可访问性和可持续性。

链接: https://arxiv.org/abs/2409.10994
作者: Dingjie Song,Wenjun Wang,Shunian Chen,Xidong Wang,Michael Guan,Benyou Wang
机构: The Chinese University of Hong Kong, Shenzhen; Lehigh University
关键词: Multimodal Large Language, Large Language Models, Multimodal Large, Large Language, advancement of Multimodal
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
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Abstract:The rapid advancement of Multimodal Large Language Models (MLLMs) has led to remarkable performances across various domains. However, this progress is accompanied by a substantial surge in the resource consumption of these models. We address this pressing issue by introducing a new approach, Token Reduction using CLIP Metric (TRIM), aimed at improving the efficiency of MLLMs without sacrificing their performance. Inspired by human attention patterns in Visual Question Answering (VQA) tasks, TRIM presents a fresh perspective on the selection and reduction of image tokens. The TRIM method has been extensively tested across 12 datasets, and the results demonstrate a significant reduction in computational overhead while maintaining a consistent level of performance. This research marks a critical stride in efficient MLLM development, promoting greater accessibility and sustainability of high-performing models.
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[NLP-91] Read Like a Radiologist: Efficient Vision-Language Model for 3D Medical Imaging Interpretation

【速读】：该论文试图解决现有医学视觉-语言模型 (VLMs) 在处理3D医学影像时面临的挑战，特别是由于计算复杂性和数据稀缺性导致的3D医学影像解释能力不足的问题。现有方法将3D医学影像分解为子体积特征，导致沿z轴的过度相关表示，忽略了切片特定的临床细节，尤其是在相邻切片冗余度较低的情况下。论文提出的解决方案是引入多切片视觉-语言模型 (MS-VLM)，该模型模仿放射科医生的工作流程，通过顺序分析单个切片并综合跨切片和视图的信息。MS-VLM利用自监督的2D变换器编码器来学习捕捉切片间依赖关系的体积表示，从而避免了子体积分块的限制，能够从任意切片长度和不同平面及相位的多幅图像中获取有用的体积表示。

链接: https://arxiv.org/abs/2412.13558
作者: Changsun Lee,Sangjoon Park,Cheong-Il Shin,Woo Hee Choi,Hyun Jeong Park,Jeong Eun Lee,Jong Chul Ye
机构: 未知
关键词: medical vision-language models, Recent medical vision-language, medical, medical image interpretation, medical image
类目: Image and Video Processing (eess.IV); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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Abstract:Recent medical vision-language models (VLMs) have shown promise in 2D medical image interpretation. However extending them to 3D medical imaging has been challenging due to computational complexities and data scarcity. Although a few recent VLMs specified for 3D medical imaging have emerged, all are limited to learning volumetric representation of a 3D medical image as a set of sub-volumetric features. Such process introduces overly correlated representations along the z-axis that neglect slice-specific clinical details, particularly for 3D medical images where adjacent slices have low redundancy. To address this limitation, we introduce MS-VLM that mimic radiologists’ workflow in 3D medical image interpretation. Specifically, radiologists analyze 3D medical images by examining individual slices sequentially and synthesizing information across slices and views. Likewise, MS-VLM leverages self-supervised 2D transformer encoders to learn a volumetric representation that capture inter-slice dependencies from a sequence of slice-specific features. Unbound by sub-volumetric patchification, MS-VLM is capable of obtaining useful volumetric representations from 3D medical images with any slice length and from multiple images acquired from different planes and phases. We evaluate MS-VLM on publicly available chest CT dataset CT-RATE and in-house rectal MRI dataset. In both scenarios, MS-VLM surpasses existing methods in radiology report generation, producing more coherent and clinically relevant reports. These findings highlight the potential of MS-VLM to advance 3D medical image interpretation and improve the robustness of medical VLMs.
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计算机视觉

[CV-0] AniDoc: Animation Creation Made Easier

【速读】：该论文旨在通过利用生成式 AI (Generative AI) 的潜力，减少二维动画制作过程中的劳动力成本。解决方案的关键在于使用视频扩散模型 (video diffusion models) 作为基础，开发出名为 AniDoc 的工具，该工具能够自动将草图序列转换为遵循参考角色规范的彩色动画。AniDoc 通过显式的对应匹配 (correspondence matching) 指导，增强了模型对参考角色与每一帧草图之间变化（如姿势）的鲁棒性。此外，该模型还能自动化中间帧生成 (in-betweening) 过程，用户只需提供角色图像以及起始和结束草图，即可轻松创建时间上一致的动画。

链接: https://arxiv.org/abs/2412.14173
作者: Yihao Meng,Hao Ouyang,Hanlin Wang,Qiuyu Wang,Wen Wang,Ka Leong Cheng,Zhiheng Liu,Yujun Shen,Huamin Qu
机构: HKUST; Ant Group; NJU; ZJU; HKU
关键词: industry-standard workflow, encompassing four essential, essential stages, character design, keyframe animation
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page and code: this https URL

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Abstract:The production of 2D animation follows an industry-standard workflow, encompassing four essential stages: character design, keyframe animation, in-betweening, and coloring. Our research focuses on reducing the labor costs in the above process by harnessing the potential of increasingly powerful generative AI. Using video diffusion models as the foundation, AniDoc emerges as a video line art colorization tool, which automatically converts sketch sequences into colored animations following the reference character specification. Our model exploits correspondence matching as an explicit guidance, yielding strong robustness to the variations (e.g., posture) between the reference character and each line art frame. In addition, our model could even automate the in-betweening process, such that users can easily create a temporally consistent animation by simply providing a character image as well as the start and end sketches. Our code is available at: this https URL.
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[CV-1] hinking in Space: How Multimodal Large Language Models See Remember and Recall Spaces

【速读】：该论文试图解决的问题是：多模态大语言模型 (Multimodal Large Language Models, MLLMs) 是否能够通过视频数据表现出视觉-空间智能，并达到接近人类的水平。解决方案的关键在于提出了一个基于视频的视觉-空间智能基准 (VSI-Bench)，包含超过5000个问答对，用于评估MLLMs的空间推理能力。研究发现，尽管MLLMs的空间推理能力仍不及人类，但模型中确实出现了局部世界模型和空间意识。论文还发现，传统的语言推理技术（如链式思维、自一致性和思维树）未能显著提升性能，而通过在问答过程中显式生成认知地图 (cognitive maps) 可以有效增强MLLMs的空间距离推理能力。

链接: https://arxiv.org/abs/2412.14171
作者: Jihan Yang,Shusheng Yang,Anjali W. Gupta,Rilyn Han,Li Fei-Fei,Saining Xie
机构: New York University; Yale University; Stanford University
关键词: sequential visual observations, Humans possess, Multimodal Large Language, visual observations, Large Language Models
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL

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Abstract:Humans possess the visual-spatial intelligence to remember spaces from sequential visual observations. However, can Multimodal Large Language Models (MLLMs) trained on million-scale video datasets also ``think in space’’ from videos? We present a novel video-based visual-spatial intelligence benchmark (VSI-Bench) of over 5,000 question-answer pairs, and find that MLLMs exhibit competitive - though subhuman - visual-spatial intelligence. We probe models to express how they think in space both linguistically and visually and find that while spatial reasoning capabilities remain the primary bottleneck for MLLMs to reach higher benchmark performance, local world models and spatial awareness do emerge within these models. Notably, prevailing linguistic reasoning techniques (e.g., chain-of-thought, self-consistency, tree-of-thoughts) fail to improve performance, whereas explicitly generating cognitive maps during question-answering enhances MLLMs’ spatial distance ability.
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[CV-2] E-CAR: Efficient Continuous Autoregressive Image Generation via Multistage Modeling

【速读】：该论文试图解决自回归模型（AR models）在图像生成中由于连续标记生成和依赖计算密集型扩散采样所带来的效率问题。解决方案的关键在于提出了ECAR（Efficient Continuous Auto-Regressive Image Generation via Multistage Modeling）方法，通过两个创新点来解决这些问题：(1) 分阶段连续标记生成策略，减少了计算复杂性，并提供了作为分层条件的渐进细化标记图；(2) 多阶段基于流的分布建模方法，在每个阶段仅转换部分去噪分布，而不是像传统扩散模型那样完全去噪。ECAR通过在增加分辨率的同时逐步去噪图像，不仅降低了标记到图像转换的成本，还实现了标记级别的并行处理，从而显著提高了计算效率并保持了图像质量。

链接: https://arxiv.org/abs/2412.14170
作者: Zhihang Yuan,Yuzhang Shang,Hanling Zhang,Tongcheng Fang,Rui Xie,Bingxin Xu,Yan Yan,Shengen Yan,Guohao Dai,Yu Wang
机构: Tsinghua University(清华大学); Infinigence AI; Illinois Tech; Shanghai Jiao Tong University(上海交通大学)
关键词: Recent advances, generation show promising, show promising results, advances in autoregressive, discrete tokenization
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:Recent advances in autoregressive (AR) models with continuous tokens for image generation show promising results by eliminating the need for discrete tokenization. However, these models face efficiency challenges due to their sequential token generation nature and reliance on computationally intensive diffusion-based sampling. We present ECAR (Efficient Continuous Auto-Regressive Image Generation via Multistage Modeling), an approach that addresses these limitations through two intertwined innovations: (1) a stage-wise continuous token generation strategy that reduces computational complexity and provides progressively refined token maps as hierarchical conditions, and (2) a multistage flow-based distribution modeling method that transforms only partial-denoised distributions at each stage comparing to complete denoising in normal diffusion models. Holistically, ECAR operates by generating tokens at increasing resolutions while simultaneously denoising the image at each stage. This design not only reduces token-to-image transformation cost by a factor of the stage number but also enables parallel processing at the token level. Our approach not only enhances computational efficiency but also aligns naturally with image generation principles by operating in continuous token space and following a hierarchical generation process from coarse to fine details. Experimental results demonstrate that ECAR achieves comparable image quality to DiT Peebles Xie [2023] while requiring 10 \times FLOPs reduction and 5 \times speedup to generate a 256 \times 256 image.
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[CV-3] Autoregressive Video Generation without Vector Quantization

【速读】：该论文试图解决高效自回归视频生成的问题，关键解决方案是将视频生成问题重新表述为非量化的自回归建模，分别进行时间上的帧预测和空间上的集合预测。与传统的栅格扫描预测或扩散模型中固定长度标记的联合分布建模不同，该方法保持了GPT风格模型的因果特性以实现灵活的上下文能力，同时在单个帧内利用双向建模提高效率。提出的NOVA模型在没有向量量化的情况下，显著提升了数据效率、推理速度、视觉保真度和视频流畅性，并在文本到图像生成任务中超越了现有的图像扩散模型。

链接: https://arxiv.org/abs/2412.14169
作者: Haoge Deng,Ting Pan,Haiwen Diao,Zhengxiong Luo,Yufeng Cui,Huchuan Lu,Shiguang Shan,Yonggang Qi,Xinlong Wang
机构: BUPT(北京邮电大学); ICT-CAS(中国科学院信息与通信技术研究所); DLUT(大连理工大学); BAAI(北京人工智能研究院)
关键词: paper presents, autoregressive, video, NOVA, high efficiency
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 22 pages, 16 figures

点击查看摘要

Abstract:This paper presents a novel approach that enables autoregressive video generation with high efficiency. We propose to reformulate the video generation problem as a non-quantized autoregressive modeling of temporal frame-by-frame prediction and spatial set-by-set prediction. Unlike raster-scan prediction in prior autoregressive models or joint distribution modeling of fixed-length tokens in diffusion models, our approach maintains the causal property of GPT-style models for flexible in-context capabilities, while leveraging bidirectional modeling within individual frames for efficiency. With the proposed approach, we train a novel video autoregressive model without vector quantization, termed NOVA. Our results demonstrate that NOVA surpasses prior autoregressive video models in data efficiency, inference speed, visual fidelity, and video fluency, even with a much smaller model capacity, i.e., 0.6B parameters. NOVA also outperforms state-of-the-art image diffusion models in text-to-image generation tasks, with a significantly lower training cost. Additionally, NOVA generalizes well across extended video durations and enables diverse zero-shot applications in one unified model. Code and models are publicly available at this https URL.
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[CV-4] FashionComposer: Compositional Fashion Image Generation

【速读】：该论文试图解决复杂时尚图像生成中的组合性问题，特别是如何灵活地处理多模态输入（如文本提示、参数化人体模型、服装图像和面部图像）并支持个性化外观、姿态和体型调整，以及在一次操作中分配多个服装。解决方案的关键在于开发了一个通用框架，能够处理多样化的输入模态，并通过构建扩展训练数据来增强模型的组合能力。此外，论文提出了“资产库”概念，将多个参考图像（如服装和面部）组织在单一图像中，并使用参考UNet提取外观特征。为了将这些特征正确注入生成结果中，提出了“主体绑定注意力”机制，将不同“资产”的外观特征与相应的文本特征绑定，从而使模型能够根据语义理解每个资产，支持任意数量和类型的参考图像。

链接: https://arxiv.org/abs/2412.14168
作者: Sihui Ji,Yiyang Wang,Xi Chen,Xiaogang Xu,Hao Luo,Hengshuang Zhao
机构: The University of Hong Kong; DAMO Academy, Alibaba Group; Zhejiang University; Hupan Lab
关键词: compositional fashion image, present FashionComposer, fashion image generation, appearance features, appearance
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: this https URL

点击查看摘要

Abstract:We present FashionComposer for compositional fashion image generation. Unlike previous methods, FashionComposer is highly flexible. It takes multi-modal input (i.e., text prompt, parametric human model, garment image, and face image) and supports personalizing the appearance, pose, and figure of the human and assigning multiple garments in one pass. To achieve this, we first develop a universal framework capable of handling diverse input modalities. We construct scaled training data to enhance the model’s robust compositional capabilities. To accommodate multiple reference images (garments and faces) seamlessly, we organize these references in a single image as an “asset library” and employ a reference UNet to extract appearance features. To inject the appearance features into the correct pixels in the generated result, we propose subject-binding attention. It binds the appearance features from different “assets” with the corresponding text features. In this way, the model could understand each asset according to their semantics, supporting arbitrary numbers and types of reference images. As a comprehensive solution, FashionComposer also supports many other applications like human album generation, diverse virtual try-on tasks, etc.
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[CV-5] VideoDPO: Omni-Preference Alignment for Video Diffusion Generation

【速读】：该论文试图解决文本到视频生成模型在生成过程中偏离用户偏好（user preferences）的问题，关键在于通过引入Direct Preference Optimization (DPO) 并进行适应性调整，提出了VideoDPO管道。与以往仅关注视觉质量或文本与视频语义对齐的图像对齐方法不同，VideoDPO综合考虑了这两个维度，并构建了一个称为OmniScore的偏好评分体系。通过自动收集基于OmniScore的偏好对数据，并根据评分对这些数据进行重新加权，显著提升了整体偏好对齐效果。实验结果表明，该方法在视觉质量和语义对齐方面均取得了显著改进，确保了各个偏好维度的全面优化。

链接: https://arxiv.org/abs/2412.14167
作者: Runtao Liu,Haoyu Wu,Zheng Ziqiang,Chen Wei,Yingqing He,Renjie Pi,Qifeng Chen
机构: HKUST(香港科技大学); Renmin University of China(中国人民大学); Johns Hopkins University(约翰斯·霍普金斯大学)
关键词: Recent progress, generative diffusion models, greatly advanced, progress in generative, Direct Preference Optimization
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:Recent progress in generative diffusion models has greatly advanced text-to-video generation. While text-to-video models trained on large-scale, diverse datasets can produce varied outputs, these generations often deviate from user preferences, highlighting the need for preference alignment on pre-trained models. Although Direct Preference Optimization (DPO) has demonstrated significant improvements in language and image generation, we pioneer its adaptation to video diffusion models and propose a VideoDPO pipeline by making several key adjustments. Unlike previous image alignment methods that focus solely on either (i) visual quality or (ii) semantic alignment between text and videos, we comprehensively consider both dimensions and construct a preference score accordingly, which we term the OmniScore. We design a pipeline to automatically collect preference pair data based on the proposed OmniScore and discover that re-weighting these pairs based on the score significantly impacts overall preference alignment. Our experiments demonstrate substantial improvements in both visual quality and semantic alignment, ensuring that no preference aspect is neglected. Code and data will be shared at this https URL.
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[CV-6] MegaSynth: Scaling Up 3D Scene Reconstruction with Synthesized Data

【速读】：该论文试图解决3D场景重建中训练数据规模不足的问题，解决方案的关键在于通过合成数据进行大规模扩展。论文提出了MegaSynth，一个程序化生成的3D数据集，包含700K个场景，比之前的真实数据集DL3DV大50倍以上。关键的创新点在于去除语义信息，转而使用基本的空间结构和几何基元来建模场景，从而简化了数据生成的复杂性并提高了可扩展性。此外，通过控制数据复杂度并与真实世界数据分布进行松散对齐，MegaSynth不仅促进了训练过程，还增强了模型在真实场景中的泛化能力。实验结果表明，使用MegaSynth进行联合训练或预训练可以显著提高重建质量，且仅在MegaSynth上训练的模型与在真实数据上训练的模型表现相当，突显了3D重建任务的低层次特性。

链接: https://arxiv.org/abs/2412.14166
作者: Hanwen Jiang,Zexiang Xu,Desai Xie,Ziwen Chen,Haian Jin,Fujun Luan,Zhixin Shu,Kai Zhang,Sai Bi,Xin Sun,Jiuxiang Gu,Qixing Huang,Georgios Pavlakos,Hao Tan
机构: The University of Texas at Austin(德克萨斯大学奥斯汀分校); Adobe Research(Adobe研究); Stony Brook University(石溪大学); Oregon State University(俄勒冈州立大学); Cornell University(康奈尔大学)
关键词: data, propose scaling, training, MegaSynth, Abstract
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL

点击查看摘要

Abstract:We propose scaling up 3D scene reconstruction by training with synthesized data. At the core of our work is MegaSynth, a procedurally generated 3D dataset comprising 700K scenes - over 50 times larger than the prior real dataset DL3DV - dramatically scaling the training data. To enable scalable data generation, our key idea is eliminating semantic information, removing the need to model complex semantic priors such as object affordances and scene composition. Instead, we model scenes with basic spatial structures and geometry primitives, offering scalability. Besides, we control data complexity to facilitate training while loosely aligning it with real-world data distribution to benefit real-world generalization. We explore training LRMs with both MegaSynth and available real data. Experiment results show that joint training or pre-training with MegaSynth improves reconstruction quality by 1.2 to 1.8 dB PSNR across diverse image domains. Moreover, models trained solely on MegaSynth perform comparably to those trained on real data, underscoring the low-level nature of 3D reconstruction. Additionally, we provide an in-depth analysis of MegaSynth’s properties for enhancing model capability, training stability, and generalization.
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[CV-7] MetaMorph: Multimodal Understanding and Generation via Instruction Tuning

【速读】：该论文试图解决如何将预训练的大型语言模型 (LLM) 快速转化为能够生成文本和视觉标记的统一自回归模型的问题。解决方案的关键在于提出了视觉预测指令调优 (Visual-Predictive Instruction Tuning, VPiT)，通过这种简单而有效的方法，LLM 能够从任何图像和文本数据序列中预测离散的文本标记和连续的视觉标记。VPiT 的关键在于其能够同时提升模型的视觉理解和生成能力，并且通过少量的生成数据即可解锁视觉生成能力，显示出 LLM 在视觉任务中具有强大的“先验”能力，可以通过相对简单的指令调优过程进行高效适应。

链接: https://arxiv.org/abs/2412.14164
作者: Shengbang Tong,David Fan,Jiachen Zhu,Yunyang Xiong,Xinlei Chen,Koustuv Sinha,Michael Rabbat,Yann LeCun,Saining Xie,Zhuang Liu
机构: Meta; NYU (纽约大学)
关键词: propose Visual-Predictive Instruction, autoregressive model capable, unified autoregressive model, Visual-Predictive Instruction Tuning, Instruction Tuning
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page at this http URL

点击查看摘要

Abstract:In this work, we propose Visual-Predictive Instruction Tuning (VPiT) - a simple and effective extension to visual instruction tuning that enables a pretrained LLM to quickly morph into an unified autoregressive model capable of generating both text and visual tokens. VPiT teaches an LLM to predict discrete text tokens and continuous visual tokens from any input sequence of image and text data curated in an instruction-following format. Our empirical investigation reveals several intriguing properties of VPiT: (1) visual generation ability emerges as a natural byproduct of improved visual understanding, and can be unlocked efficiently with a small amount of generation data; (2) while we find understanding and generation to be mutually beneficial, understanding data contributes to both capabilities more effectively than generation data. Building upon these findings, we train our MetaMorph model and achieve competitive performance on both visual understanding and generation. In visual generation, MetaMorph can leverage the world knowledge and reasoning abilities gained from LLM pretraining, and overcome common failure modes exhibited by other generation models. Our results suggest that LLMs may have strong “prior” vision capabilities that can be efficiently adapted to both visual understanding and generation with a relatively simple instruction tuning process.
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[CV-8] AKiRa: Augmentation Kit on Rays for optical video generation

【速读】：该论文试图解决现有文本条件视频扩散方法在摄像机控制方面的局限性，特别是对动态摄像机运动、变焦、镜头畸变和焦点偏移等光学参数的控制不足的问题。解决方案的关键在于提出了AKiRa（Augmentation Kit on Rays）框架，通过在现有视频生成骨干网络上构建和训练一个复杂的摄像机模型适配器，实现了对摄像机运动和光学参数（如焦距、畸变、光圈）的精细控制，从而生成具有电影效果的视频，如变焦、鱼眼效果和散景。实验结果表明，AKiRa在结合和编排摄像机光学效果方面优于所有最先进的方法，为未来的光学视频生成方法奠定了基础。

链接: https://arxiv.org/abs/2412.14158
作者: Xi Wang,Robin Courant,Marc Christie,Vicky Kalogeiton
机构: LIX, École Polytechnique, IP Paris(LIX, 巴黎综合理工学院, IP Paris); Univ Rennes, IRISA, Inria, CNRS(雷恩大学, IRISA, Inria, 法国国家科学研究中心)
关键词: Recent advances, improved video quality, text-conditioned video diffusion, greatly improved video, advances in text-conditioned
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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点击查看摘要

Abstract:Recent advances in text-conditioned video diffusion have greatly improved video quality. However, these methods offer limited or sometimes no control to users on camera aspects, including dynamic camera motion, zoom, distorted lens and focus shifts. These motion and optical aspects are crucial for adding controllability and cinematic elements to generation frameworks, ultimately resulting in visual content that draws focus, enhances mood, and guides emotions according to filmmakers’ controls. In this paper, we aim to close the gap between controllable video generation and camera optics. To achieve this, we propose AKiRa (Augmentation Kit on Rays), a novel augmentation framework that builds and trains a camera adapter with a complex camera model over an existing video generation backbone. It enables fine-tuned control over camera motion as well as complex optical parameters (focal length, distortion, aperture) to achieve cinematic effects such as zoom, fisheye effect, and bokeh. Extensive experiments demonstrate AKiRa’s effectiveness in combining and composing camera optics while outperforming all state-of-the-art methods. This work sets a new landmark in controlled and optically enhanced video generation, paving the way for future optical video generation methods.
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[CV-9] MCMat: Multiview-Consistent and Physically Accurate PBR Material Generation

【速读】：该论文试图解决现有2D方法在生成多视角物理渲染（PBR）贴图时存在的多视角不一致问题，以及3D方法在直接生成UV贴图时由于3D数据有限导致的泛化性问题。解决方案的关键在于提出了一种两阶段方法：第一阶段采用Diffusion Transformer (DiT)模型生成PBR材质，通过多分支DiT和基于参考的DiT块中的全局注意力机制促进不同视角间的特征交互与融合，提升多视角一致性，并采用基于PBR的扩散损失确保生成的材质符合物理原理；第二阶段提出材质精炼DiT，在UV空间中进行空区域的修复和细节增强，同时以生成阶段的材质图为条件，降低学习难度并提高泛化能力。

链接: https://arxiv.org/abs/2412.14148
作者: Shenhao Zhu,Lingteng Qiu,Xiaodong Gu,Zhengyi Zhao,Chao Xu,Yuxiao He,Zhe Li,Xiaoguang Han,Yao Yao,Xun Cao,Siyu Zhu,Weihao Yuan,Zilong Dong,Hao Zhu
机构: Nanjing University(南京大学); Alibaba Group(阿里巴巴集团); Huazhong University of Science and Technology(华中科技大学); Fudan University(复旦大学); SSE, CUHKSZ(深圳香港中文大学商学院); FNii, CUHKSZ(深圳香港中文大学未来网络研究院)
关键词: multi-view physically-based rendering, utilize UNet-based diffusion, encountering generalization issues, generalization issues due, methods utilize UNet-based
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL

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Abstract:Existing 2D methods utilize UNet-based diffusion models to generate multi-view physically-based rendering (PBR) maps but struggle with multi-view inconsistency, while some 3D methods directly generate UV maps, encountering generalization issues due to the limited 3D data. To address these problems, we propose a two-stage approach, including multi-view generation and UV materials refinement. In the generation stage, we adopt a Diffusion Transformer (DiT) model to generate PBR materials, where both the specially designed multi-branch DiT and reference-based DiT blocks adopt a global attention mechanism to promote feature interaction and fusion between different views, thereby improving multi-view consistency. In addition, we adopt a PBR-based diffusion loss to ensure that the generated materials align with realistic physical principles. In the refinement stage, we propose a material-refined DiT that performs inpainting in empty areas and enhances details in UV space. Except for the normal condition, this refinement also takes the material map from the generation stage as an additional condition to reduce the learning difficulty and improve generalization. Extensive experiments show that our method achieves state-of-the-art performance in texturing 3D objects with PBR materials and provides significant advantages for graphics relighting applications. Project Page: this https URL
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[CV-10] Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation

【速读】：该论文试图解决视觉理解中从图像级到像素级的语义分割问题，特别是如何在开放词汇环境下实现高效的语义分割。解决方案的关键在于提出了一种名为特征金字塔标记化 (Feature Pyramid Tokenization, PAT) 的方法，通过可学习的码本对多分辨率特征进行聚类和表示，并结合像素重建和语义分割的联合学习，实现了像素和语义的统一标记压缩。PAT 设计了松耦合的像素和语义学习分支，像素分支模拟自底向上和自顶向下的码本标记组合与可视化，而语义分支则融合层次化的码本作为辅助分割指导。该方法不仅提升了视觉语言模型 (VLM) 特征金字塔的语义直觉，还在开放词汇语义分割基准上取得了竞争性表现，同时保持了参数高效性和灵活性。

链接: https://arxiv.org/abs/2412.14145
作者: Jianyu Zhang,Li Zhang,Shijian Li
机构: Zhejiang University (浙江大学)
关键词: Open Vocabulary semantic, Vocabulary semantic segmentation, semantic, Vocabulary semantic, Open Vocabulary
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages, 6 figures

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Abstract:The visual understanding are often approached from 3 granular levels: image, patch and pixel. Visual Tokenization, trained by self-supervised reconstructive learning, compresses visual data by codebook in patch-level with marginal information loss, but the visual tokens does not have semantic meaning. Open Vocabulary semantic segmentation benefits from the evolving Vision-Language models (VLMs) with strong image zero-shot capability, but transferring image-level to pixel-level understanding remains an imminent challenge. In this paper, we treat segmentation as tokenizing pixels and study a united perceptual and semantic token compression for all granular understanding and consequently facilitate open vocabulary semantic segmentation. Referring to the cognitive process of pretrained VLM where the low-level features are progressively composed to high-level semantics, we propose Feature Pyramid Tokenization (PAT) to cluster and represent multi-resolution feature by learnable codebooks and then decode them by joint learning pixel reconstruction and semantic segmentation. We design loosely coupled pixel and semantic learning branches. The pixel branch simulates bottom-up composition and top-down visualization of codebook tokens, while the semantic branch collectively fuse hierarchical codebooks as auxiliary segmentation guidance. Our experiments show that PAT enhances the semantic intuition of VLM feature pyramid, improves performance over the baseline segmentation model and achieves competitive performance on open vocabulary semantic segmentation benchmark. Our model is parameter-efficient for VLM integration and flexible for the independent tokenization. We hope to give inspiration not only on improving segmentation but also on semantic visual token utilization.
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[CV-11] AnySat: An Earth Observation Model for Any Resolutions Scales and Modalities

【速读】：该论文试图解决地理空间模型在处理多样化地球观测数据（如不同分辨率、尺度和模态）时的适应性问题。现有方法通常要求固定的输入配置，限制了其实际应用。解决方案的关键在于提出了AnySat模型，该模型基于联合嵌入预测架构（JEPA）和分辨率自适应空间编码器，能够在自监督学习框架下对高度异构的数据进行训练。通过构建包含5个多模态数据集和11种不同传感器的GeoPlex数据集，并训练单一模型处理这些多样化的数据，AnySat在环境监测任务（如土地覆盖映射、树种识别、作物分类、变化检测和洪水分割）中实现了优于或接近最先进的结果。

链接: https://arxiv.org/abs/2412.14123
作者: Guillaume Astruc,Nicolas Gonthier,Clement Mallet,Loic Landrieu
机构: 未知
关键词: Earth observation data, diversity of Earth, Earth observation, terms of resolutions, Geospatial models
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Geospatial models must adapt to the diversity of Earth observation data in terms of resolutions, scales, and modalities. However, existing approaches expect fixed input configurations, which limits their practical applicability. We propose AnySat, a multimodal model based on joint embedding predictive architecture (JEPA) and resolution-adaptive spatial encoders, allowing us to train a single model on highly heterogeneous data in a self-supervised manner. To demonstrate the advantages of this unified approach, we compile GeoPlex, a collection of 5 multimodal datasets with varying characteristics and 11 distinct sensors. We then train a single powerful model on these diverse datasets simultaneously. Once fine-tuned, we achieve better or near state-of-the-art results on the datasets of GeoPlex and 4 additional ones for 5 environment monitoring tasks: land cover mapping, tree species identification, crop type classification, change detection, and flood segmentation. The code and models are available at this https URL.
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[CV-12] GaraMoSt: Parallel Multi-Granularity Motion and Structural Modeling for Efficient Multi-Frame Interpolation in DSA Images AAAI2025

【速读】：该论文试图解决在数字减影血管造影 (Digital Subtraction Angiography, DSA) 图像中直接进行多帧插值时，现有方法（如 MoSt-DSA）在实时性能下对高频噪声抑制不足和低频噪声过滤不彻底的问题。解决方案的关键在于提出了 GaraMoSt 方法，通过优化网络架构，采用并行设计的 MG-MSFE 模块，该模块能够在全卷积并行方式下提取多粒度的帧间运动和结构特征，并支持不同尺度下上下文感知粒度的独立灵活调整，从而在保持计算效率的同时提升插值精度和噪声抑制效果。

链接: https://arxiv.org/abs/2412.14118
作者: Ziyang Xu,Huangxuan Zhao,Wenyu Liu,Xinggang Wang
机构: 未知
关键词: Digital Subtraction Angiography, Subtraction Angiography, Digital Subtraction, accurate direct multi-frame, direct multi-frame interpolation
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by AAAI2025

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Abstract:The rapid and accurate direct multi-frame interpolation method for Digital Subtraction Angiography (DSA) images is crucial for reducing radiation and providing real-time assistance to physicians for precise diagnostics and treatment. DSA images contain complex vascular structures and various motions. Applying natural scene Video Frame Interpolation (VFI) methods results in motion artifacts, structural dissipation, and blurriness. Recently, MoSt-DSA has specifically addressed these issues for the first time and achieved SOTA results. However, MoSt-DSA’s focus on real-time performance leads to insufficient suppression of high-frequency noise and incomplete filtering of low-frequency noise in the generated images. To address these issues within the same computational time scale, we propose GaraMoSt. Specifically, we optimize the network pipeline with a parallel design and propose a module named MG-MSFE. MG-MSFE extracts frame-relative motion and structural features at various granularities in a fully convolutional parallel manner and supports independent, flexible adjustment of context-aware granularity at different scales, thus enhancing computational efficiency and accuracy. Extensive experiments demonstrate that GaraMoSt achieves the SOTA performance in accuracy, robustness, visual effects, and noise suppression, comprehensively surpassing MoSt-DSA and other natural scene VFI methods. The code and models are available at this https URL.
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[CV-13] Event-based Photometric Bundle Adjustment

【速读】：该论文试图解决纯旋转事件相机中的捆绑调整（bundle adjustment）问题，即同时优化相机姿态和场景地图。解决方案的关键在于提出了一种基于事件的光度捆绑调整方法（Event-based Photometric Bundle Adjustment, EPBA），该方法直接利用事件生成模型定义光度误差，并在相机旋转和半密集场景亮度上进行优化。EPBA通过利用事件数据的稀疏性设计了一种可处理的Levenberg-Marquardt求解器，能够有效处理大量变量。与传统方法不同，EPBA不需将事件转换为类似图像的表示，而是直接利用事件数据的空间-时间特性，从而显著降低光度误差（最多可达90%），并在合成和真实数据集上展示了其优越性。

链接: https://arxiv.org/abs/2412.14111
作者: Shuang Guo,Guillermo Gallego
机构: Technische Universität Berlin(柏林工业大学); Robotics Institute Germany(德国机器人研究所); Science of Intelligence (SCIoI) Excellence Cluster(智能科学卓越集群); Einstein Center Digital Future (ECDF)(数字未来爱因斯坦中心)
关键词: purely rotating event, Photometric Bundle Adjustment, rotating event camera, bundle adjustment, simultaneous refinement
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO); Signal Processing (eess.SP); Optimization and Control (math.OC)
备注: 21 pages, 19 figures, 10 tables. Project page: this https URL

点击查看摘要

Abstract:We tackle the problem of bundle adjustment (i.e., simultaneous refinement of camera poses and scene map) for a purely rotating event camera. Starting from first principles, we formulate the problem as a classical non-linear least squares optimization. The photometric error is defined using the event generation model directly in the camera rotations and the semi-dense scene brightness that triggers the events. We leverage the sparsity of event data to design a tractable Levenberg-Marquardt solver that handles the very large number of variables involved. To the best of our knowledge, our method, which we call Event-based Photometric Bundle Adjustment (EPBA), is the first event-only photometric bundle adjustment method that works on the brightness map directly and exploits the space-time characteristics of event data, without having to convert events into image-like representations. Comprehensive experiments on both synthetic and real-world datasets demonstrate EPBA’s effectiveness in decreasing the photometric error (by up to 90%), yielding results of unparalleled quality. The refined maps reveal details that were hidden using prior state-of-the-art rotation-only estimation methods. The experiments on modern high-resolution event cameras show the applicability of EPBA to panoramic imaging in various scenarios (without map initialization, at multiple resolutions, and in combination with other methods, such as IMU dead reckoning or previous event-based rotation estimation methods). We make the source code publicly available. this https URL
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[CV-14] Foundation Models Meet Low-Cost Sensors: Test-Time Adaptation for Rescaling Disparity for Zero-Shot Metric Depth Estimation

【速读】：该论文试图解决单目深度估计中零样本深度估计的度量深度恢复问题。传统方法通过微调模型来实现，但这一过程既耗时又可能降低模型的泛化能力。论文提出的解决方案关键在于利用低成本传感器（如低分辨率LiDAR、立体相机、运动结构法结合IMU提供的位姿）提供的3D点来重新缩放Depth Anything模型的预测结果，从而避免微调过程并保持原始模型的泛化能力，同时对传感器噪声和深度模型噪声具有鲁棒性。

链接: https://arxiv.org/abs/2412.14103
作者: Rémi Marsal,Alexandre Chapoutot,Philippe Xu,David Filliat
机构: U2IS, ENSTA ParisInstitut Polytechnique de Paris
关键词: zero-shot monocular depth, monocular depth estimation, monocular depth, zero-shot monocular, depth estimation
类目: Computer Vision and Pattern Recognition (cs.CV)
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点击查看摘要

Abstract:The recent development of foundation models for monocular depth estimation such as Depth Anything paved the way to zero-shot monocular depth estimation. Since it returns an affine-invariant disparity map, the favored technique to recover the metric depth consists in fine-tuning the model. However, this stage is costly to perform because of the training but also due to the creation of the dataset. It must contain images captured by the camera that will be used at test time and the corresponding ground truth. Moreover, the fine-tuning may also degrade the generalizing capacity of the original model. Instead, we propose in this paper a new method to rescale Depth Anything predictions using 3D points provided by low-cost sensors or techniques such as low-resolution LiDAR, stereo camera, structure-from-motion where poses are given by an IMU. Thus, this approach avoids fine-tuning and preserves the generalizing power of the original depth estimation model while being robust to the noise of the sensor or of the depth model. Our experiments highlight improvements relative to other metric depth estimation methods and competitive results compared to fine-tuned approaches. Code available at this https URL.
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[CV-15] Adaptive Concept Bottleneck for Foundation Models Under Distribution Shifts ICML2024

【速读】：该论文试图解决基础模型（Foundation Models, FMs）在实际应用中由于输入分布变化导致的解释性不足和性能下降问题。解决方案的关键在于提出了一种自适应概念瓶颈模型（Concept Bottleneck Models, CBMs）框架，该框架能够在目标域的无标签数据基础上动态调整概念向量库和预测层，从而在不依赖源训练数据的情况下，提升模型在测试数据上的解释性和准确性，实验结果显示该方法可将部署后的准确性提高多达28%。

链接: https://arxiv.org/abs/2412.14097
作者: Jihye Choi,Jayaram Raghuram,Yixuan Li,Somesh Jha
机构: University of Wisconsin-Madison (威斯康星大学麦迪逊分校)
关键词: machine learning, Concept Bottleneck Models, non-interpretable foundation models, foundation models, Advancements in foundation
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: The preliminary version of the work appeared in the ICML 2024 Workshop on Foundation Models in the Wild

点击查看摘要

Abstract:Advancements in foundation models (FMs) have led to a paradigm shift in machine learning. The rich, expressive feature representations from these pre-trained, large-scale FMs are leveraged for multiple downstream tasks, usually via lightweight fine-tuning of a shallow fully-connected network following the representation. However, the non-interpretable, black-box nature of this prediction pipeline can be a challenge, especially in critical domains such as healthcare, finance, and security. In this paper, we explore the potential of Concept Bottleneck Models (CBMs) for transforming complex, non-interpretable foundation models into interpretable decision-making pipelines using high-level concept vectors. Specifically, we focus on the test-time deployment of such an interpretable CBM pipeline “in the wild”, where the input distribution often shifts from the original training distribution. We first identify the potential failure modes of such a pipeline under different types of distribution shifts. Then we propose an adaptive concept bottleneck framework to address these failure modes, that dynamically adapts the concept-vector bank and the prediction layer based solely on unlabeled data from the target domain, without access to the source (training) dataset. Empirical evaluations with various real-world distribution shifts show that our adaptation method produces concept-based interpretations better aligned with the test data and boosts post-deployment accuracy by up to 28%, aligning the CBM performance with that of non-interpretable classification.
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[CV-16] Joint Perception and Prediction for Autonomous Driving: A Survey

【速读】：该论文试图解决传统自动驾驶系统中感知和预测模块独立开发和优化所导致的计算资源不共享、误差传播放大以及信息丢失等问题。解决方案的关键在于采用联合感知和预测范式，通过多任务学习将感知和预测模块整合为一个统一模型。这种方法不仅克服了传统方法的局限性，还使三个任务（目标检测、目标跟踪和运动预测）能够直接访问原始传感器数据，从而实现对环境更丰富和细致的解释。

链接: https://arxiv.org/abs/2412.14088
作者: Lucas Dal’Col,Miguel Oliveira,Vítor Santos
机构: University of Aveiro (UA); Department of Mechanical Engineering (DEM); Intelligent System Associate Laboratory (LASI); Institute of Electronics and Informatics Engineering of Aveiro (IEETA)
关键词: enabling vehicles, critical components, vehicles to navigate, navigate safely, safely through complex
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 24 pages, 5 sections, 7 figures, 7 tables. This work has been submitted to the IEEE Transactions on Intelligent Transportation Systems for possible publication

点击查看摘要

Abstract:Perception and prediction modules are critical components of autonomous driving systems, enabling vehicles to navigate safely through complex environments. The perception module is responsible for perceiving the environment, including static and dynamic objects, while the prediction module is responsible for predicting the future behavior of these objects. These modules are typically divided into three tasks: object detection, object tracking, and motion prediction. Traditionally, these tasks are developed and optimized independently, with outputs passed sequentially from one to the next. However, this approach has significant limitations: computational resources are not shared across tasks, the lack of joint optimization can amplify errors as they propagate throughout the pipeline, and uncertainty is rarely propagated between modules, resulting in significant information loss. To address these challenges, the joint perception and prediction paradigm has emerged, integrating perception and prediction into a unified model through multi-task learning. This strategy not only overcomes the limitations of previous methods, but also enables the three tasks to have direct access to raw sensor data, allowing richer and more nuanced environmental interpretations. This paper presents the first comprehensive survey of joint perception and prediction for autonomous driving. We propose a taxonomy that categorizes approaches based on input representation, scene context modeling, and output representation, highlighting their contributions and limitations. Additionally, we present a qualitative analysis and quantitative comparison of existing methods. Finally, we discuss future research directions based on identified gaps in the state-of-the-art.
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[CV-17] owards Generalist Robot Policies: What Matters in Building Vision-Language-Action Models

【速读】：该论文试图解决从视觉语言模型 (Vision-Language Models, VLMs) 到视觉语言动作模型 (Vision-Language-Action Models, VLAs) 的转化过程中，由于不同模型的骨干网络、动作预测公式、数据分布和训练方法的差异，导致缺乏系统性设计理解的问题。解决方案的关键在于揭示影响VLA性能的关键因素，并聚焦于三个核心设计选择：选择合适的骨干网络、如何构建VLA架构，以及何时引入跨实体数据。通过广泛的实验（涵盖8种VLM骨干网络、4种策略架构和600多次实验），论文提出了一种新型VLA框架——RoboVLMs，该框架具有高度灵活性，支持新VLM的轻松集成和多种设计选择的自由组合，并在模拟任务和实际实验中达到了新的性能水平。论文还公开了所有相关细节，包括代码、模型、数据集和工具包，以促进未来研究。

链接: https://arxiv.org/abs/2412.14058
作者: Xinghang Li,Peiyan Li,Minghuan Liu,Dong Wang,Jirong Liu,Bingyi Kang,Xiao Ma,Tao Kong,Hanbo Zhang,Huaping Liu
机构: Tsinghua University(清华大学); ByteDance Research(字节跳动研究); CASIA MAIS-NLPR; Shanghai Jiao Tong University(上海交通大学); National University of Singapore(新加坡国立大学)
关键词: Foundation Vision Language, Vision Language Models, Foundation Vision, Vision Language, exhibit strong capabilities
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this http URL

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Abstract:Foundation Vision Language Models (VLMs) exhibit strong capabilities in multi-modal representation learning, comprehension, and reasoning. By injecting action components into the VLMs, Vision-Language-Action Models (VLAs) can be naturally formed and also show promising performance. Existing work has demonstrated the effectiveness and generalization of VLAs in multiple scenarios and tasks. Nevertheless, the transfer from VLMs to VLAs is not trivial since existing VLAs differ in their backbones, action-prediction formulations, data distributions, and training recipes. This leads to a missing piece for a systematic understanding of the design choices of VLAs. In this work, we disclose the key factors that significantly influence the performance of VLA and focus on answering three essential design choices: which backbone to select, how to formulate the VLA architectures, and when to add cross-embodiment data. The obtained results convince us firmly to explain why we need VLA and develop a new family of VLAs, RoboVLMs, which require very few manual designs and achieve a new state-of-the-art performance in three simulation tasks and real-world experiments. Through our extensive experiments, which include over 8 VLM backbones, 4 policy architectures, and over 600 distinct designed experiments, we provide a detailed guidebook for the future design of VLAs. In addition to the study, the highly flexible RoboVLMs framework, which supports easy integrations of new VLMs and free combinations of various design choices, is made public to facilitate future research. We open-source all details, including codes, models, datasets, and toolkits, along with detailed training and evaluation recipes at: this http URL.
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[CV-18] CAD-Recode: Reverse Engineering CAD Code from Point Clouds

【速读】：该论文试图解决3D CAD逆向工程问题，即从点云等3D表示中重建CAD草图和操作序列。解决方案的关键在于三个层面的创新：CAD序列表示、网络设计和数据集。具体而言，论文提出将CAD草图-拉伸序列表示为Python代码，并通过CAD-Recode模型将点云转换为可执行的Python代码，从而重建CAD模型。该方法利用预训练的大型语言模型（LLM）作为解码器，结合轻量级点云投影器，并在一个包含一百万个多样化CAD序列的合成数据集上进行训练。CAD-Recode在多个数据集上显著优于现有方法，尤其是在DeepCAD和Fusion360数据集上，其平均Chamfer距离比最先进的方法低10倍，同时所需的输入点更少。此外，生成的Python代码可被现成的LLM解释，支持CAD编辑和基于点云的CAD特定问题解答。

链接: https://arxiv.org/abs/2412.14042
作者: Danila Rukhovich,Elona Dupont,Dimitrios Mallis,Kseniya Cherenkova,Anis Kacem,Djamila Aouada
机构: 未知
关键词: sequentially drawing parametric, drawing parametric sketches, applying CAD operations, CAD, Python code
类目: Computer Vision and Pattern Recognition (cs.CV)
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点击查看摘要

Abstract:Computer-Aided Design (CAD) models are typically constructed by sequentially drawing parametric sketches and applying CAD operations to obtain a 3D model. The problem of 3D CAD reverse engineering consists of reconstructing the sketch and CAD operation sequences from 3D representations such as point clouds. In this paper, we address this challenge through novel contributions across three levels: CAD sequence representation, network design, and dataset. In particular, we represent CAD sketch-extrude sequences as Python code. The proposed CAD-Recode translates a point cloud into Python code that, when executed, reconstructs the CAD model. Taking advantage of the exposure of pre-trained Large Language Models (LLMs) to Python code, we leverage a relatively small LLM as a decoder for CAD-Recode and combine it with a lightweight point cloud projector. CAD-Recode is trained solely on a proposed synthetic dataset of one million diverse CAD sequences. CAD-Recode significantly outperforms existing methods across three datasets while requiring fewer input points. Notably, it achieves 10 times lower mean Chamfer distance than state-of-the-art methods on DeepCAD and Fusion360 datasets. Furthermore, we show that our CAD Python code output is interpretable by off-the-shelf LLMs, enabling CAD editing and CAD-specific question answering from point clouds.
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[CV-19] SurgSora: Decoupled RGBD-Flow Diffusion Model for Controllable Surgical Video Generation

【速读】：该论文试图解决医学视频生成中的可控性和真实性问题。解决方案的关键在于提出了SurgSora框架，该框架通过单一输入帧和用户可控的运动提示生成手术视频。其核心组件包括：Dual Semantic Injector (DSI)用于提取并整合RGB、深度特征和分割线索，捕捉复杂解剖结构的空间细节；Decoupled Flow Mapper (DFM)通过多尺度融合光流和语义-RGB-D特征，增强时间理解和物体空间动态；Trajectory Controller (TC)允许用户指定运动方向并估计稀疏光流，指导视频生成过程。最终，融合的特征作为条件输入到冻结的Stable Diffusion模型中，生成逼真且时间一致的手术视频。

链接: https://arxiv.org/abs/2412.14018
作者: Tong Chen,Shuya Yang,Junyi Wang,Long Bai,Hongliang Ren,Luping Zhou
机构: 未知
关键词: controllable visual representations, surgical video generation, video generation, enhancing surgical understanding, visual representations
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Multimedia (cs.MM); Robotics (cs.RO)
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点击查看摘要

Abstract:Medical video generation has transformative potential for enhancing surgical understanding and pathology insights through precise and controllable visual representations. However, current models face limitations in controllability and authenticity. To bridge this gap, we propose SurgSora, a motion-controllable surgical video generation framework that uses a single input frame and user-controllable motion cues. SurgSora consists of three key modules: the Dual Semantic Injector (DSI), which extracts object-relevant RGB and depth features from the input frame and integrates them with segmentation cues to capture detailed spatial features of complex anatomical structures; the Decoupled Flow Mapper (DFM), which fuses optical flow with semantic-RGB-D features at multiple scales to enhance temporal understanding and object spatial dynamics; and the Trajectory Controller (TC), which allows users to specify motion directions and estimates sparse optical flow, guiding the video generation process. The fused features are used as conditions for a frozen Stable Diffusion model to produce realistic, temporally coherent surgical videos. Extensive evaluations demonstrate that SurgSora outperforms state-of-the-art methods in controllability and authenticity, showing its potential to advance surgical video generation for medical education, training, and research.
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[CV-20] Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation

【速读】：该论文试图解决在深度基础模型中引入提示机制以实现高分辨率度量深度估计的问题。解决方案的关键在于通过低成本的LiDAR作为提示，引导Depth Anything模型生成精确的度量深度输出，并采用多尺度提示融合设计在深度解码器中集成LiDAR信息。此外，为了应对训练数据有限的挑战，论文提出了一种可扩展的数据处理流程，包括合成数据LiDAR模拟和真实数据伪GT深度生成，从而在ARKitScenes和ScanNet++数据集上实现了新的最先进性能，并促进了3D重建和通用机器人抓取等下游应用。

链接: https://arxiv.org/abs/2412.14015
作者: Haotong Lin,Sida Peng,Jingxiao Chen,Songyou Peng,Jiaming Sun,Minghuan Liu,Hujun Bao,Jiashi Feng,Xiaowei Zhou,Bingyi Kang
机构: Zhejiang University(浙江大学); ByteDance Seed(字节跳动种子); Shanghai Jiao Tong University(上海交通大学); ETH Zurich(苏黎世联邦理工学院)
关键词: vision foundation models, depth foundation models, specific tasks, foundation models, play a critical
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL

点击查看摘要

Abstract:Prompts play a critical role in unleashing the power of language and vision foundation models for specific tasks. For the first time, we introduce prompting into depth foundation models, creating a new paradigm for metric depth estimation termed Prompt Depth Anything. Specifically, we use a low-cost LiDAR as the prompt to guide the Depth Anything model for accurate metric depth output, achieving up to 4K resolution. Our approach centers on a concise prompt fusion design that integrates the LiDAR at multiple scales within the depth decoder. To address training challenges posed by limited datasets containing both LiDAR depth and precise GT depth, we propose a scalable data pipeline that includes synthetic data LiDAR simulation and real data pseudo GT depth generation. Our approach sets new state-of-the-arts on the ARKitScenes and ScanNet++ datasets and benefits downstream applications, including 3D reconstruction and generalized robotic grasping.
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[CV-21] InstructSeg: Unifying Instructed Visual Segmentation with Multi-modal Large Language Models

【速读】：该论文试图解决图像和视频领域中，文本引导的通用分割模型在不同领域分别开发的问题，忽略了这两个领域任务设置和解决方案的相似性。解决方案的关键在于提出了InstructSeg，一个端到端的分割流水线，配备了多模态大语言模型 (Multi-modal Large Language Models, MLLMs)，用于定义为图像和视频级别的指示视觉分割 (Instructed Visual Segmentation, IVS)。具体来说，InstructSeg通过使用对象感知的视频感知器来提取参考帧中的时间和对象信息，以促进对视频的全面理解，并引入了视觉引导的多粒度文本融合，以更好地将全局和详细的文本信息与细粒度的视觉指导相结合。通过多任务和端到端训练，InstructSeg在多种图像和视频分割任务中表现出优越的性能，超越了专门的分割模型和基于MLLM的方法。

链接: https://arxiv.org/abs/2412.14006
作者: Cong Wei,Yujie Zhong,Haoxian Tan,Yingsen Zeng,Yong Liu,Zheng Zhao,Yujiu Yang
机构: Tsinghua Shenzhen International Graduate School, Tsinghua University(清华大学深圳国际研究生院); Meituan Inc.(美团公司)
关键词: Multi-modal Large Language, Large Language Models, Boosted by Multi-modal, Multi-modal Large, Large Language
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Boosted by Multi-modal Large Language Models (MLLMs), text-guided universal segmentation models for the image and video domains have made rapid progress recently. However, these methods are often developed separately for specific domains, overlooking the similarities in task settings and solutions across these two areas. In this paper, we define the union of referring segmentation and reasoning segmentation at both the image and video levels as Instructed Visual Segmentation (IVS). Correspondingly, we propose InstructSeg, an end-to-end segmentation pipeline equipped with MLLMs for IVS. Specifically, we employ an object-aware video perceiver to extract temporal and object information from reference frames, facilitating comprehensive video understanding. Additionally, we introduce vision-guided multi-granularity text fusion to better integrate global and detailed text information with fine-grained visual guidance. By leveraging multi-task and end-to-end training, InstructSeg demonstrates superior performance across diverse image and video segmentation tasks, surpassing both segmentation specialists and MLLM-based methods with a single model. Our code is available at this https URL.
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[CV-22] Real-Time Position-Aware View Synthesis from Single-View Input

【速读】：该论文试图解决实时视图合成（real-time view synthesis）中的性能瓶颈问题，特别是在需要低延迟的实时应用中，现有的高质量视图合成方法难以满足实时性要求。解决方案的关键在于提出了一种轻量级的、位置感知网络（position-aware network），该网络通过多层感知机（multi-layer perceptron）建模的位置感知嵌入（Position Aware Embedding），将目标相机姿态的位置信息高效映射为高维特征图。这些特征图与输入图像结合，通过渲染网络（Rendering Network）的双编码器分支融合高层语义和低层细节，生成逼真的新视图，从而在不依赖显式几何操作（如变形）的情况下实现高效的实时视图合成。

链接: https://arxiv.org/abs/2412.14005
作者: Manu Gond,Emin Zerman,Sebastian Knorr,Mårten Sjöström
机构: Mid Sweden UniversitySundsvallSweden; Technical University of BerlinBerlinGermany; HTW Berlin - University of Applied SciencesBerlinGermany
关键词: significantly enhanced immersive, enhanced immersive experiences, Recent advancements, view synthesis, including telepresence
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Multimedia (cs.MM)
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Abstract:Recent advancements in view synthesis have significantly enhanced immersive experiences across various computer graphics and multimedia applications, including telepresence, and entertainment. By enabling the generation of new perspectives from a single input view, view synthesis allows users to better perceive and interact with their environment. However, many state-of-the-art methods, while achieving high visual quality, face limitations in real-time performance, which makes them less suitable for live applications where low latency is critical. In this paper, we present a lightweight, position-aware network designed for real-time view synthesis from a single input image and a target camera pose. The proposed framework consists of a Position Aware Embedding, modeled with a multi-layer perceptron, which efficiently maps positional information from the target pose to generate high dimensional feature maps. These feature maps, along with the input image, are fed into a Rendering Network that merges features from dual encoder branches to resolve both high level semantics and low level details, producing a realistic new view of the scene. Experimental results demonstrate that our method achieves superior efficiency and visual quality compared to existing approaches, particularly in handling complex translational movements without explicit geometric operations like warping. This work marks a step toward enabling real-time view synthesis from a single image for live and interactive applications.
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[CV-23] GraphAvatar: Compact Head Avatars with GNN-Generated 3D Gaussians AAAI2025

【速读】：该论文试图解决从任意视角渲染逼真头部虚拟形象时，现有基于神经辐射场 (NeRF) 和三维高斯喷射 (3DGS) 方法在保真度和效率上的不足，特别是存储开销过大的问题。解决方案的关键在于提出了 GraphAvatar 方法，利用图神经网络 (GNN) 生成三维高斯分布的属性，从而将存储需求大幅降低至仅 10MB。具体而言，GraphAvatar 通过训练几何 GNN 和外观 GNN 从跟踪的网格中生成三维高斯属性，并引入图引导优化模块来减少面部跟踪误差的影响，同时使用三维感知增强器进行后处理以提升渲染质量。

链接: https://arxiv.org/abs/2412.13983
作者: Xiaobao Wei,Peng Chen,Ming Lu,Hui Chen,Feng Tian
机构: 1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China (计算机科学与技术学院，哈尔滨工业大学，哈尔滨，中国);
2. Key Laboratory of Network Security and Privacy Protection, Harbin Institute of Technology, Harbin, China (网络安全与隐私保护重点实验室，哈尔滨工业大学，哈尔滨，中国);
3. School of Software, Harbin Institute of Technology, Harbin, China (软件学院，哈尔滨工业大学，哈尔滨，中国)
关键词: Neural Radiance Fields, Graph Neural Networks, Rendering photorealistic head, photorealistic head avatars, virtual reality
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: accepted by AAAI2025

点击查看摘要

Abstract:Rendering photorealistic head avatars from arbitrary viewpoints is crucial for various applications like virtual reality. Although previous methods based on Neural Radiance Fields (NeRF) can achieve impressive results, they lack fidelity and efficiency. Recent methods using 3D Gaussian Splatting (3DGS) have improved rendering quality and real-time performance but still require significant storage overhead. In this paper, we introduce a method called GraphAvatar that utilizes Graph Neural Networks (GNN) to generate 3D Gaussians for the head avatar. Specifically, GraphAvatar trains a geometric GNN and an appearance GNN to generate the attributes of the 3D Gaussians from the tracked mesh. Therefore, our method can store the GNN models instead of the 3D Gaussians, significantly reducing the storage overhead to just 10MB. To reduce the impact of face-tracking errors, we also present a novel graph-guided optimization module to refine face-tracking parameters during training. Finally, we introduce a 3D-aware enhancer for post-processing to enhance the rendering quality. We conduct comprehensive experiments to demonstrate the advantages of GraphAvatar, surpassing existing methods in visual fidelity and storage consumption. The ablation study sheds light on the trade-offs between rendering quality and model size. The code will be released at: this https URL
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[CV-24] Real Classification by Description: Extending CLIPs Limits of Part Attributes Recognition

【速读】：该论文试图解决通过描述性属性进行零样本“真实”分类的问题，即评估视觉-语言模型（Vision-Language Models, VLMs）如CLIP在仅依赖描述性属性而非对象类别名称的情况下进行分类的能力。解决方案的关键在于：1) 引入新的挑战并发布描述数据，以鼓励研究社区进行真正的零样本学习；2) 通过使用ImageNet21k的多样化对象类别和大型语言模型生成的丰富属性描述，对CLIP进行针对性训练，以增强其属性检测能力；3) 提出一种改进的CLIP架构，利用多分辨率来提升细粒度部分属性的检测能力。这些方法共同提升了CLIP在六个流行基准测试和PACO数据集上的细粒度分类性能。

链接: https://arxiv.org/abs/2412.13947
作者: Ethan Baron,Idan Tankel,Peter Tu,Guy Ben-Yosef
机构: GE HealthCare Technology and Innovation Center(GE医疗技术和创新中心); GE Aerospace Research(GE航空航天研究)
关键词: excluding object class, classify objects based, objects based solely, tackle zero shot, define and tackle
类目: Computer Vision and Pattern Recognition (cs.CV)
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点击查看摘要

Abstract:In this study, we define and tackle zero shot “real” classification by description, a novel task that evaluates the ability of Vision-Language Models (VLMs) like CLIP to classify objects based solely on descriptive attributes, excluding object class names. This approach highlights the current limitations of VLMs in understanding intricate object descriptions, pushing these models beyond mere object recognition. To facilitate this exploration, we introduce a new challenge and release description data for six popular fine-grained benchmarks, which omit object names to encourage genuine zero-shot learning within the research community. Additionally, we propose a method to enhance CLIP’s attribute detection capabilities through targeted training using ImageNet21k’s diverse object categories, paired with rich attribute descriptions generated by large language models. Furthermore, we introduce a modified CLIP architecture that leverages multiple resolutions to improve the detection of fine-grained part attributes. Through these efforts, we broaden the understanding of part-attribute recognition in CLIP, improving its performance in fine-grained classification tasks across six popular benchmarks, as well as in the PACO dataset, a widely used benchmark for object-attribute recognition. Code is available at: this https URL.
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[CV-25] On Explaining Knowledge Distillation: Measuring and Visualising the Knowledge Transfer Process WACV’25

【速读】：该论文试图解决知识蒸馏 (Knowledge Distillation, KD) 过程中知识传递过程不透明的问题，关键解决方案是提出了UniCAM，一种基于梯度的视觉解释方法，用于有效解读KD过程中学生模型从教师模型中学习到的知识。通过UniCAM，学生模型在教师知识的指导下能够更高效地学习相关特征（蒸馏特征）并忽略不相关特征（残余特征）。论文还引入了两个新指标：特征相似度分数 (Feature Similarity Score, FSS) 和相关性分数 (Relevance Score, RS)，用于量化蒸馏知识的相关性，从而为解释KD过程提供了有价值的见解。

链接: https://arxiv.org/abs/2412.13943
作者: Gereziher Adhane,Mohammad Mahdi Dehshibi,Dennis Vetter,David Masip,Gemma Roig
机构: Universitat Oberta de Catalunya; Universidad Carlos III de Madrid; Goethe University Frankfurt
关键词: remains challenging due, knowledge transfer process, remains challenging, making it difficult, challenging due
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted to 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV’25). Includes 5 pages of supplementary material

点击查看摘要

Abstract:Knowledge distillation (KD) remains challenging due to the opaque nature of the knowledge transfer process from a Teacher to a Student, making it difficult to address certain issues related to KD. To address this, we proposed UniCAM, a novel gradient-based visual explanation method, which effectively interprets the knowledge learned during KD. Our experimental results demonstrate that with the guidance of the Teacher’s knowledge, the Student model becomes more efficient, learning more relevant features while discarding those that are not relevant. We refer to the features learned with the Teacher’s guidance as distilled features and the features irrelevant to the task and ignored by the Student as residual features. Distilled features focus on key aspects of the input, such as textures and parts of objects. In contrast, residual features demonstrate more diffused attention, often targeting irrelevant areas, including the backgrounds of the target objects. In addition, we proposed two novel metrics: the feature similarity score (FSS) and the relevance score (RS), which quantify the relevance of the distilled knowledge. Experiments on the CIFAR10, ASIRRA, and Plant Disease datasets demonstrate that UniCAM and the two metrics offer valuable insights to explain the KD process.
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[CV-26] Retrieval Augmented Image Harmonization

【速读】：该论文试图解决图像嵌入（foreground）到背景图像（background）时，由于光照条件差异导致的图像协调问题。现有方法在背景中缺乏与前景相似内容时，协调结果不可靠，且在存在相似内容时，协调过程易受无关区域干扰。解决方案的关键在于提出了一种检索增强的图像协调框架（Retrieval-Augmented Image Harmonization, Raiha），通过检索与前景物体相似且光照条件一致的参考图像，减少问题的病态性，并通过引入图像内容先验（image content priors）来确保合理的注意力分配，从而提升协调效果。

链接: https://arxiv.org/abs/2412.13916
作者: Haolin Wang,Ming Liu,Zifei Yan,Chao Zhou,Longan Xiao,Wangmeng Zuo
机构: School of Computer Science and Technology, Harbin Institute of Technology(哈尔滨工业大学计算机科学与技术学院); TRANSSION(传音控股)
关键词: image harmonization, perform image harmonization, foreground object coordinate, image, harmonization
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages

点击查看摘要

Abstract:When embedding objects (foreground) into images (background), considering the influence of photography conditions like illumination, it is usually necessary to perform image harmonization to make the foreground object coordinate with the background image in terms of brightness, color, and etc. Although existing image harmonization methods have made continuous efforts toward visually pleasing results, they are still plagued by two main issues. Firstly, the image harmonization becomes highly ill-posed when there are no contents similar to the foreground object in the background, making the harmonization results unreliable. Secondly, even when similar contents are available, the harmonization process is often interfered with by irrelevant areas, mainly attributed to an insufficient understanding of image contents and inaccurate attention. As a remedy, we present a retrieval-augmented image harmonization (Raiha) framework, which seeks proper reference images to reduce the ill-posedness and restricts the attention to better utilize the useful information. Specifically, an efficient retrieval method is designed to find reference images that contain similar objects as the foreground while the illumination is consistent with the background. For training the Raiha framework to effectively utilize the reference information, a data augmentation strategy is delicately designed by leveraging existing non-reference image harmonization datasets. Besides, the image content priors are introduced to ensure reasonable attention. With the presented Raiha framework, the image harmonization performance is greatly boosted under both non-reference and retrieval-augmented settings. The source code and pre-trained models will be publicly available.
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[CV-27] A Black-Box Evaluation Framework for Semantic Robustness in Birds Eye View Detection

【速读】：该论文旨在解决基于相机鸟瞰图（BEV）感知模型在自动驾驶领域中的鲁棒性问题，特别是针对多视角BEV检测任务中随机生成的语义扰动（natural corruptions）的影响。论文提出了一种黑箱鲁棒性评估框架，通过对抗性优化三种常见的语义扰动（几何变换、颜色偏移和运动模糊）来欺骗BEV模型，这是该领域首次采用此类方法。解决方案的关键在于设计了一个基于距离的平滑替代函数来替代传统的mAP指标，并引入了SimpleDIRECT算法，该算法利用观测斜率来指导优化过程。通过与随机扰动和两种优化基线的比较，验证了该框架的有效性，并提供了十个近期BEV模型的语义鲁棒性基准测试结果。

链接: https://arxiv.org/abs/2412.13913
作者: Fu Wang,Yanghao Zhang,Xiangyu Yin,Guangliang Cheng,Zeyu Fu,Xiaowei Huang,Wenjie Ruan
机构: 1. School of Computer Science and Technology, Soochow University(苏州大学计算机科学与技术学院);
2. Jiangsu Key Laboratory of Big Data Analysis Technology(江苏省大数据分析技术重点实验室);
3. School of Software, Soochow University(苏州大学软件学院)
关键词: Camera-based Bird Eye, Bird Eye View, receive increasing attention, Camera-based Bird, Eye View
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Camera-based Bird’s Eye View (BEV) perception models receive increasing attention for their crucial role in autonomous driving, a domain where concerns about the robustness and reliability of deep learning have been raised. While only a few works have investigated the effects of randomly generated semantic perturbations, aka natural corruptions, on the multi-view BEV detection task, we develop a black-box robustness evaluation framework that adversarially optimises three common semantic perturbations: geometric transformation, colour shifting, and motion blur, to deceive BEV models, serving as the first approach in this emerging field. To address the challenge posed by optimising the semantic perturbation, we design a smoothed, distance-based surrogate function to replace the mAP metric and introduce SimpleDIRECT, a deterministic optimisation algorithm that utilises observed slopes to guide the optimisation process. By comparing with randomised perturbation and two optimisation baselines, we demonstrate the effectiveness of the proposed framework. Additionally, we provide a benchmark on the semantic robustness of ten recent BEV models. The results reveal that PolarFormer, which emphasises geometric information from multi-view images, exhibits the highest robustness, whereas BEVDet is fully compromised, with its precision reduced to zero.
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[CV-28] Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer

【速读】：该论文试图解决在体积医学图像分割领域中，由于标注数据有限导致预训练的Segment Anything Models (SAMs)性能受限的问题。解决方案的关键在于引入记忆机制（memory mechanism），通过记忆和回忆过去输入的内部表示来提升SAM的性能，同时保持较低的计算成本。具体而言，论文提出了Memorizing SAM，一种结合记忆Transformer的3D SAM架构，利用现有高精度的内部表示作为记忆源，确保记忆质量。实验结果表明，Memorizing SAM在TotalSegmentator数据集的33个类别中，平均Dice系数提升了11.36%，而推理时间仅增加了4.38毫秒。

链接: https://arxiv.org/abs/2412.13908
作者: Xinyuan Shao,Yiqing Shen,Mathias Unberath
机构: Johns Hopkins University, Baltimore, MD, USA (约翰斯·霍普金斯大学，巴尔的摩，马里兰州，美国)
关键词: Segment Anything Models, gained increasing attention, zero-shot generalization capability, image analysis due, medical image analysis
类目: Computer Vision and Pattern Recognition (cs.CV)
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点击查看摘要

Abstract:Segment Anything Models (SAMs) have gained increasing attention in medical image analysis due to their zero-shot generalization capability in segmenting objects of unseen classes and domains when provided with appropriate user prompts. Addressing this performance gap is important to fully leverage the pre-trained weights of SAMs, particularly in the domain of volumetric medical image segmentation, where accuracy is important but well-annotated 3D medical data for fine-tuning is limited. In this work, we investigate whether introducing the memory mechanism as a plug-in, specifically the ability to memorize and recall internal representations of past inputs, can improve the performance of SAM with limited computation cost. To this end, we propose Memorizing SAM, a novel 3D SAM architecture incorporating a memory Transformer as a plug-in. Unlike conventional memorizing Transformers that save the internal representation during training or inference, our Memorizing SAM utilizes existing highly accurate internal representation as the memory source to ensure the quality of memory. We evaluate the performance of Memorizing SAM in 33 categories from the TotalSegmentator dataset, which indicates that Memorizing SAM can outperform state-of-the-art 3D SAM variant i.e., FastSAM3D with an average Dice increase of 11.36% at the cost of only 4.38 millisecond increase in inference time. The source code is publicly available at this https URL
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[CV-29] Data-Efficient Inference of Neural Fluid Fields via SciML Foundation Model

【速读】：该论文试图解决在推断真实世界三维流体动力学时，依赖密集视频序列和专业实验室设备导致的高成本和复杂性问题。解决方案的关键在于利用科学机器学习（SciML）基础模型，这些模型通过预训练的偏微分方程（PDE）模拟，编码了丰富的多物理场知识，从而提供了有效的领域先验。论文提出了一种协作训练方法，通过增强视图和基础模型提取的流体特征来改进神经流体场的推断，显著提高了数据效率和泛化能力，并在定量指标和视觉质量上展示了显著的改进。

链接: https://arxiv.org/abs/2412.13897
作者: Yuqiu Liu,Jingxuan Xu,Mauricio Soroco,Yunchao Wei,Wuyang Chen
机构: Simon Fraser University(西蒙弗雷泽大学); Beijing Jiaotong University(北京交通大学); Peng Cheng Laboratory(鹏城实验室)
关键词: enabled successful progress, Recent developments, foundation models, enabled successful, successful progress
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Recent developments in 3D vision have enabled successful progress in inferring neural fluid fields and realistic rendering of fluid dynamics. However, these methods require real-world flow captures, which demand dense video sequences and specialized lab setups, making the process costly and challenging. Scientific machine learning (SciML) foundation models, which are pretrained on extensive simulations of partial differential equations (PDEs), encode rich multiphysics knowledge and thus provide promising sources of domain priors for inferring fluid fields. Nevertheless, their potential to advance real-world vision problems remains largely underexplored, raising questions about the transferability and practical utility of these foundation models. In this work, we demonstrate that SciML foundation model can significantly improve the data efficiency of inferring real-world 3D fluid dynamics with improved generalization. At the core of our method is leveraging the strong forecasting capabilities and meaningful representations of SciML foundation models. We equip neural fluid fields with a novel collaborative training approach that utilizes augmented views and fluid features extracted by our foundation model. Our method demonstrates significant improvements in both quantitative metrics and visual quality, showcasing the practical applicability of SciML foundation models in real-world fluid dynamics.
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[CV-30] Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset

【速读】：该论文试图解决腕部骨折等细微病理在X光片中的自动识别问题，尤其是在医生缺乏专业解读能力的情况下，提升诊断准确性。解决方案的关键在于将腕部病理识别视为细粒度视觉识别 (Fine-Grained Visual Recognition, FGVR) 问题，并采用基于FGVR的集成方法来识别X光片中的判别区域。通过使用可解释AI (Explainable AI, XAI) 技术如Grad-CAM来定位这些区域，该集成方法显著提升了识别精度，超越了许多传统和现有的FGVR技术。

链接: https://arxiv.org/abs/2412.13884
作者: Ammar Ahmed,Ali Shariq Imran,Mohib Ullah,Zenun Kastrati,Sher Muhammad Daudpota
机构: Norwegian University of Science & Technology (NTNU)(挪威科技大学); Linnaeus University (林奈大学); Sukkur IBA University (苏库尔IBA大学)
关键词: gained considerable research, considerable research attention, recent years, exploration of automated, gained considerable
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:The exploration of automated wrist fracture recognition has gained considerable research attention in recent years. In practical medical scenarios, physicians and surgeons may lack the specialized expertise required for accurate X-ray interpretation, highlighting the need for machine vision to enhance diagnostic accuracy. However, conventional recognition techniques face challenges in discerning subtle differences in X-rays when classifying wrist pathologies, as many of these pathologies, such as fractures, can be small and hard to distinguish. This study tackles wrist pathology recognition as a fine-grained visual recognition (FGVR) problem, utilizing a limited, custom-curated dataset that mirrors real-world medical constraints, relying solely on image-level annotations. We introduce a specialized FGVR-based ensemble approach to identify discriminative regions within X-rays. We employ an Explainable AI (XAI) technique called Grad-CAM to pinpoint these regions. Our ensemble approach outperformed many conventional SOTA and FGVR techniques, underscoring the effectiveness of our strategy in enhancing accuracy in wrist pathology recognition.
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[CV-31] Denoising Nearest Neighbor Graph via Continuous CRF for Visual Re-ranking without Fine-tuning

【速读】：该论文试图解决基于最近邻图 (Nearest Neighbor graph, NN graph) 的视觉重排序中存在的噪声边问题，即由于错误连接负样本图像而导致检索质量下降的问题。解决方案的关键在于提出了一种基于连续条件随机场 (Continuous Conditional Random Field, C-CRF) 的互补去噪方法，该方法利用基于相似性分布的统计距离，并通过团 (clique) 的概念来确保计算可行性。该方法通过应用于三种视觉重排序方法，显著提升了地标检索和行人重识别 (person re-identification, re-ID) 的质量。

链接: https://arxiv.org/abs/2412.13875
作者: Jaeyoon Kim,Yoonki Cho,Taeyong Kim,Sung-Eui Yoon
机构: KAIST(韩国科学技术院); Samsung Electronics(三星电子)
关键词: Nearest Neighbor graph, Nearest Neighbor, Neighbor graph, high retrieval accuracy, Visual re-ranking
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Visual re-ranking using Nearest Neighbor graph~(NN graph) has been adapted to yield high retrieval accuracy, since it is beneficial to exploring an high-dimensional manifold and applicable without additional fine-tuning. The quality of visual re-ranking using NN graph, however, is limited to that of connectivity, i.e., edges of the NN graph. Some edges can be misconnected with negative images. This is known as a noisy edge problem, resulting in a degradation of the retrieval quality. To address this, we propose a complementary denoising method based on Continuous Conditional Random Field (C-CRF) that uses a statistical distance of our similarity-based distribution. This method employs the concept of cliques to make the process computationally feasible. We demonstrate the complementarity of our method through its application to three visual re-ranking methods, observing quality boosts in landmark retrieval and person re-identification (re-ID).
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[CV-32] LLaVA-UHD v2: an MLLM Integrating High-Resolution Feature Pyramid via Hierarchical Window Transformer

【速读】：该论文试图解决多模态大语言模型 (MLLMs) 中视觉编码器（如 Vision Transformers, ViTs）在处理通用任务时性能不足的问题，主要原因是缺乏来自不同视觉层次的信息，导致无法与语言生成所需的多样化语义粒度对齐。解决方案的关键在于提出 LLaVA-UHD v2，其核心是分层窗口 Transformer (Hierarchical window transformer)，通过构建和整合高分辨率特征金字塔来捕捉多层次的视觉粒度。具体实现包括两个主要模块：(i) 通过 ViT 派生的特征上采样过程构建的逆特征金字塔，利用图像金字塔中的高频细节；(ii) 分层窗口注意力机制，聚焦于跨尺度窗口中的关键采样特征，以压缩多层次特征图。实验表明，LLaVA-UHD v2 在多个基准测试中显著优于现有 MLLMs，平均提升 3.7%，在 DocVQA 上提升 9.3%。

链接: https://arxiv.org/abs/2412.13871
作者: Yipeng Zhang,Yifan Liu,Zonghao Guo,Yidan Zhang,Xuesong Yang,Chi Chen,Jun Song,Bo Zheng,Yuan Yao,Zhiyuan Liu,Tat-Seng Chua,Maosong Sun
机构: Tsinghua University(清华大学); National University of Singapore(新加坡国立大学); Alibaba Group(阿里巴巴集团); University of Chinese Academy of Sciences(中国科学院大学); Aerospace Information Research Institute, Chinese Academy of Sciences(中国科学院空天信息创新研究院)
关键词: multimodal large language, multimodal large, widely employed, large language models, visual encoding
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:In multimodal large language models (MLLMs), vision transformers (ViTs) are widely employed for visual encoding. However, their performance in solving universal MLLM tasks is not satisfactory. We attribute it to a lack of information from diverse visual levels, impeding alignment with the various semantic granularity required for language generation. To address this issue, we present LLaVA-UHD v2, an advanced MLLM centered around a Hierarchical window transformer that enables capturing diverse visual granularity by constructing and integrating a high-resolution feature pyramid. As a vision-language projector, Hiwin transformer comprises two primary modules: (i) an inverse feature pyramid, constructed by a ViT-derived feature up-sampling process utilizing high-frequency details from an image pyramid, and (ii) hierarchical window attention, focusing on a set of key sampling features within cross-scale windows to condense multi-level feature maps. Extensive experiments demonstrate that LLaVA-UHD v2 achieves superior performance over existing MLLMs on popular benchmarks. Notably, our design brings an average boost of 3.7% across 14 benchmarks compared with the baseline method, 9.3% on DocVQA for instance. We make all the data, model checkpoint, and code publicly available to facilitate future research.
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[CV-33] Zero-Shot Prompting and Few-Shot Fine-Tuning: Revisiting Document Image Classification Using Large Language Models ICPR2024

【速读】：该论文试图解决在文档分类任务中减少对大量人类标注训练样本依赖的问题。解决方案的关键在于利用大型语言模型（LLMs）的零样本提示（zero-shot prompting）和少样本微调（few-shot model fine-tuning）技术，以实现在仅使用少量甚至无需任何训练样本的情况下，达到接近完美的分类性能。

链接: https://arxiv.org/abs/2412.13859
作者: Anna Scius-Bertrand,Michael Jungo,Lars Vögtlin,Jean-Marc Spat,Andreas Fischer
机构: 未知
关键词: Classifying scanned documents, Classifying scanned, involves image, text analysis, training samples
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Classifying scanned documents is a challenging problem that involves image, layout, and text analysis for document understanding. Nevertheless, for certain benchmark datasets, notably RVL-CDIP, the state of the art is closing in to near-perfect performance when considering hundreds of thousands of training samples. With the advent of large language models (LLMs), which are excellent few-shot learners, the question arises to what extent the document classification problem can be addressed with only a few training samples, or even none at all. In this paper, we investigate this question in the context of zero-shot prompting and few-shot model fine-tuning, with the aim of reducing the need for human-annotated training samples as much as possible.
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[CV-34] A Systematic Analysis of Input Modalities for Fracture Classification of the Paediatric Wrist

【速读】：该论文试图解决儿童和青少年远端前臂骨折分类的准确性问题，并探讨了在现有X光片（radiographs）基础上，结合自动骨分割（automatic bone segmentation）、骨折位置（fracture location）和放射学报告（radiology reports）等额外信息对分类性能的提升效果。解决方案的关键在于系统性地分析这些额外信息类型的贡献，通过将这些信息与X光片结合，显著提高了分类的AUROC（从91.71提升至93.25），从而为骨折分类提供了更精确的工具。

链接: https://arxiv.org/abs/2412.13856
作者: Ron Keuth,Maren Balks,Sebastian Tschauner,Ludger Tüshaus,Mattias Heinrich
机构: University of Lübeck (吕贝克大学)
关键词: cases treated annually, annually in Germany, distal forearm, children and adolescents, cases treated
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Code available on this https URL

点击查看摘要

Abstract:Fractures, particularly in the distal forearm, are among the most common injuries in children and adolescents, with approximately 800 000 cases treated annually in Germany. The AO/OTA system provides a structured fracture type classification, which serves as the foundation for treatment decisions. Although accurately classifying fractures can be challenging, current deep learning models have demonstrated performance comparable to that of experienced radiologists. While most existing approaches rely solely on radiographs, the potential impact of incorporating other additional modalities, such as automatic bone segmentation, fracture location, and radiology reports, remains underexplored. In this work, we systematically analyse the contribution of these three additional information types, finding that combining them with radiographs increases the AUROC from 91.71 to 93.25. Our code is available on GitHub.
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[CV-35] MobiFuse: A High-Precision On-device Depth Perception System with Multi-Data Fusion

【速读】：该论文试图解决移动设备上高精度深度感知的问题，提出了MobiFuse系统，通过结合双RGB摄像头和飞行时间（Time-of-Flight, ToF）摄像头来实现。解决方案的关键在于引入深度误差指示（Depth Error Indication, DEI）模态，用于表征ToF和立体匹配的深度误差，并采用渐进融合策略，将ToF和立体深度图的几何特征与DEI模态的深度误差特征融合，生成精确的深度图。此外，论文还创建了新的ToF-Stereo深度数据集RealToF，用于模型训练和验证。实验结果表明，MobiFuse显著减少了深度测量误差，最高可达77.7%，并在3D重建和3D分割等下游任务中表现出强大的泛化能力。

链接: https://arxiv.org/abs/2412.13848
作者: Jinrui Zhang,Deyu Zhang,Tingting Long,Wenxin Chen,Ju Ren,Yunxin Liu,Yudong Zhao,Yaoxue Zhang,Youngki Lee
机构: School of Computer Science and Engineering, Central South University(中南大学计算机科学与工程学院); Department of Computer Science and Technology, Tsinghua University(清华大学计算机科学与技术系); Institute for AI Industry Research (AIR), Tsinghua University(清华大学人工智能产业研究院); SHANGHAI TRANSSION CO., LTD(上海传音控股有限公司); Department of Computer Science and Engineering, Seoul National University(首尔国立大学计算机科学与工程系)
关键词: combines dual RGB, dual RGB, high-precision depth perception, depth perception system, Depth Error Indication
类目: Computer Vision and Pattern Recognition (cs.CV)
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点击查看摘要

Abstract:We present MobiFuse, a high-precision depth perception system on mobile devices that combines dual RGB and Time-of-Flight (ToF) cameras. To achieve this, we leverage physical principles from various environmental factors to propose the Depth Error Indication (DEI) modality, characterizing the depth error of ToF and stereo-matching. Furthermore, we employ a progressive fusion strategy, merging geometric features from ToF and stereo depth maps with depth error features from the DEI modality to create precise depth maps. Additionally, we create a new ToF-Stereo depth dataset, RealToF, to train and validate our model. Our experiments demonstrate that MobiFuse excels over baselines by significantly reducing depth measurement errors by up to 77.7%. It also showcases strong generalization across diverse datasets and proves effectiveness in two downstream tasks: 3D reconstruction and 3D segmentation. The demo video of MobiFuse in real-life scenarios is available at the de-identified YouTube link(this https URL).
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[CV-36] Do Language Models Understand Time?

【速读】：该论文试图解决的问题是大型语言模型（LLMs）在视频处理中的时间推理能力不足。尽管LLMs在视频理解任务中表现出色，但其与预训练视频编码器之间的交互存在关键限制，特别是在建模长期依赖关系和抽象时间概念（如因果关系和事件进展）方面。解决方案的关键在于探索未来发展方向，包括LLMs与编码器的协同进化、开发带有明确时间标签的丰富数据集，以及设计创新架构以整合空间、时间和语义推理。通过解决这些挑战，论文旨在提升LLMs的时间理解能力，从而充分发挥其在视频分析及其他领域的潜力。

链接: https://arxiv.org/abs/2412.13845
作者: Xi Ding,Lei Wang
机构: Australian National University (澳大利亚国立大学)
关键词: Large language models, computer vision applications, revolutionized video-based computer, video-based computer vision, Large language
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Research report

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Abstract:Large language models (LLMs) have revolutionized video-based computer vision applications, including action recognition, anomaly detection, and video summarization. Videos inherently pose unique challenges, combining spatial complexity with temporal dynamics that are absent in static images or textual data. Current approaches to video understanding with LLMs often rely on pretrained video encoders to extract spatiotemporal features and text encoders to capture semantic meaning. These representations are integrated within LLM frameworks, enabling multimodal reasoning across diverse video tasks. However, the critical question persists: Can LLMs truly understand the concept of time, and how effectively can they reason about temporal relationships in videos? This work critically examines the role of LLMs in video processing, with a specific focus on their temporal reasoning capabilities. We identify key limitations in the interaction between LLMs and pretrained encoders, revealing gaps in their ability to model long-term dependencies and abstract temporal concepts such as causality and event progression. Furthermore, we analyze challenges posed by existing video datasets, including biases, lack of temporal annotations, and domain-specific limitations that constrain the temporal understanding of LLMs. To address these gaps, we explore promising future directions, including the co-evolution of LLMs and encoders, the development of enriched datasets with explicit temporal labels, and innovative architectures for integrating spatial, temporal, and semantic reasoning. By addressing these challenges, we aim to advance the temporal comprehension of LLMs, unlocking their full potential in video analysis and beyond.
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[CV-37] Prompt Categories Cluster for Weakly Supervised Semantic Segmentation

【速读】：该论文试图解决弱监督语义分割 (Weakly Supervised Semantic Segmentation, WSSS) 中由于类间语义模糊导致的错误激活问题。传统方法主要通过增强类间差异来避免这一问题，但忽略了相似类别之间共享信息的作用。论文提出的解决方案关键在于引入了一种新的 WSSS 框架，称为提示类别聚类 (Prompt Categories Clustering, PCC)，通过利用大型语言模型 (Large Language Models, LLMs) 的能力，基于提示生成类别聚类，从而识别并利用相似类别间的共享信息。这种聚类关系被整合到训练网络中，帮助模型更好地学习类别间的隐含联系，进而提升语义分割性能。实验结果表明，该方法在 PASCAL VOC 2012 数据集上表现优异，并超越了现有的 WSSS 最先进方法。

链接: https://arxiv.org/abs/2412.13823
作者: Wangyu Wu,Xianglin Qiu,Siqi Song,Xiaowei Huang,Fei Ma,Jimin Xiao
机构: Xi’an Jiaotong-Liverpool University(西交利物浦大学); The University of Liverpool(利物浦大学)
关键词: Weakly Supervised Semantic, Supervised Semantic Segmentation, Weakly Supervised, leverages image-level labels, garnered significant attention
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Weakly Supervised Semantic Segmentation (WSSS), which leverages image-level labels, has garnered significant attention due to its cost-effectiveness. The previous methods mainly strengthen the inter-class differences to avoid class semantic ambiguity which may lead to erroneous activation. However, they overlook the positive function of some shared information between similar classes. Categories within the same cluster share some similar features. Allowing the model to recognize these features can further relieve the semantic ambiguity between these classes. To effectively identify and utilize this shared information, in this paper, we introduce a novel WSSS framework called Prompt Categories Clustering (PCC). Specifically, we explore the ability of Large Language Models (LLMs) to derive category clusters through prompts. These clusters effectively represent the intrinsic relationships between categories. By integrating this relational information into the training network, our model is able to better learn the hidden connections between categories. Experimental results demonstrate the effectiveness of our approach, showing its ability to enhance performance on the PASCAL VOC 2012 dataset and surpass existing state-of-the-art methods in WSSS.
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[CV-38] Nullu: Mitigating Object Hallucinations in Large Vision-Language Models via HalluSpace Projection

【速读】：该论文试图解决大视觉-语言模型 (LVLMs) 中存在的对象幻觉 (Object Hallucinations, OH) 问题。解决方案的关键在于引入了一种基于不安全子空间（称为 HalluSpace）的模型权重编辑方法，名为 Nullu。通过提取幻觉嵌入特征并去除真实表示，HalluSpace 被识别出来，并通过正交化模型权重将输入特征投影到 HalluSpace 的零空间 (Null space) 中，从而减少 OH。该方法通过抑制大语言模型 (LLMs) 的统计偏差和单模态先验来过滤幻觉特征，生成上下文准确的输出。实验表明，Nullu 方法在不同 LVLM 家族中有效减少了 OH，且无需额外的推理成本，同时在通用 LVLM 基准测试中表现出色。

链接: https://arxiv.org/abs/2412.13817
作者: Le Yang,Ziwei Zheng,Boxu Chen,Zhengyu Zhao,Chenhao Lin,Chao Shen
机构: Xi’an Jiaotong University (西安交通大学)
关键词: Recent studies, large vision-language models, object hallucinations, vision-language models, model weights based
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Under review

点击查看摘要

Abstract:Recent studies have shown that large vision-language models (LVLMs) often suffer from the issue of object hallucinations (OH). To mitigate this issue, we introduce an efficient method that edits the model weights based on an unsafe subspace, which we call HalluSpace in this paper. With truthful and hallucinated text prompts accompanying the visual content as inputs, the HalluSpace can be identified by extracting the hallucinated embedding features and removing the truthful representations in LVLMs. By orthogonalizing the model weights, input features will be projected into the Null space of the HalluSpace to reduce OH, based on which we name our method Nullu. We reveal that HalluSpaces generally contain statistical bias and unimodal priors of the large language models (LLMs) applied to build LVLMs, which have been shown as essential causes of OH in previous studies. Therefore, null space projection suppresses the LLMs’ priors to filter out the hallucinated features, resulting in contextually accurate outputs. Experiments show that our method can effectively mitigate OH across different LVLM families without extra inference costs and also show strong performance in general LVLM benchmarks. Code is released at \urlthis https URL.
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[CV-39] Object Style Diffusion for Generalized Object Detection in Urban Scene

【速读】：该论文试图解决深度学习方法在目标检测任务中对大量标注数据的依赖问题，特别是在复杂和不可预测的真实环境中，这种依赖性显著限制了现有目标检测技术的泛化能力。解决方案的关键在于提出了一种名为GoDiff的单域目标检测泛化方法，核心是利用预训练模型和伪目标数据生成模块（Pseudo Target Data Generation, PTDG）。PTDG通过潜在扩散模型生成保留源域特征并引入风格变化的伪目标域数据，从而多样化训练数据集。此外，引入跨风格实例归一化技术，融合不同域的风格特征，增强检测器的鲁棒性。实验结果表明，该方法不仅提升了现有检测器的泛化能力，还可作为其他单域泛化方法的即插即用增强工具，在自动驾驶场景中达到最先进的性能。

链接: https://arxiv.org/abs/2412.13815
作者: Hao Li,Xiangyuan Yang,Mengzhu Wang,Long Lan,Ke Liang,Xinwang Liu,Kenli Li
机构: Hao Li1; Xiangyuan Yang2; Mengzhu Wang1; Long Lan1; Ke Liang1; Xinwang Liu1; Kenli Li3
关键词: urban scene monitoring, computer vision, scene monitoring, critical task, task in computer
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Object detection is a critical task in computer vision, with applications in various domains such as autonomous driving and urban scene monitoring. However, deep learning-based approaches often demand large volumes of annotated data, which are costly and difficult to acquire, particularly in complex and unpredictable real-world environments. This dependency significantly hampers the generalization capability of existing object detection techniques. To address this issue, we introduce a novel single-domain object detection generalization method, named GoDiff, which leverages a pre-trained model to enhance generalization in unseen domains. Central to our approach is the Pseudo Target Data Generation (PTDG) module, which employs a latent diffusion model to generate pseudo-target domain data that preserves source domain characteristics while introducing stylistic variations. By integrating this pseudo data with source domain data, we diversify the training dataset. Furthermore, we introduce a cross-style instance normalization technique to blend style features from different domains generated by the PTDG module, thereby increasing the detector’s robustness. Experimental results demonstrate that our method not only enhances the generalization ability of existing detectors but also functions as a plug-and-play enhancement for other single-domain generalization methods, achieving state-of-the-art performance in autonomous driving scenarios.
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[CV-40] CAD-Assistant: Tool-Augmented VLLM s as Generic CAD Task Solvers?

【速读】：该论文试图解决的是在计算机辅助设计 (CAD) 领域中，如何利用人工智能 (AI) 辅助设计的问题。解决方案的关键在于提出了一个通用的 CAD 代理 (CAD-Assistant)，该代理基于强大的视觉和大型语言模型 (VLLM) 作为规划器，并通过工具增强范式使用 CAD 特定模块。CAD-Assistant 通过生成可在 Python 解释器上迭代执行的操作来处理多模态用户查询，该解释器配备了通过其 Python API 访问的 FreeCAD 软件。该框架能够评估生成的 CAD 命令对几何形状的影响，并根据 CAD 设计状态的变化调整后续操作。通过结合多种 CAD 特定工具（如 Python 库、FreeCAD Python API 模块、实用程序、渲染功能和其他专用模块），CAD-Assistant 展示了作为通用 CAD 任务解决器的潜力，适用于多样化的 CAD 工作流程。

链接: https://arxiv.org/abs/2412.13810
作者: Dimitrios Mallis,Ahmet Serdar Karadeniz,Sebastian Cavada,Danila Rukhovich,Niki Foteinopoulou,Kseniya Cherenkova,Anis Kacem,Djamila Aouada
机构: SnT, University of Luxembourg(SnT, 卢森堡大学); Artec3D, Luxembourg(Artec3D, 卢森堡)
关键词: Large Language Model, general-purpose CAD agent, Python API, agent for AI-assisted, Language Model
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
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Abstract:We propose CAD-Assistant, a general-purpose CAD agent for AI-assisted design. Our approach is based on a powerful Vision and Large Language Model (VLLM) as a planner and a tool-augmentation paradigm using CAD-specific modules. CAD-Assistant addresses multimodal user queries by generating actions that are iteratively executed on a Python interpreter equipped with the FreeCAD software, accessed via its Python API. Our framework is able to assess the impact of generated CAD commands on geometry and adapts subsequent actions based on the evolving state of the CAD design. We consider a wide range of CAD-specific tools including Python libraries, modules of the FreeCAD Python API, helpful routines, rendering functions and other specialized modules. We evaluate our method on multiple CAD benchmarks and qualitatively demonstrate the potential of tool-augmented VLLMs as generic CAD task solvers across diverse CAD workflows.
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[CV-41] M3-VOS: Multi-Phase Multi-Transition and Multi-Scenery Video Object Segmentation

【速读】：该论文试图解决动态物体在相变过程中视觉分割被忽视的问题。解决方案的关键在于引入了基于物体视觉特征和形态变化的分段相位概念，并提出了一个新的基准测试——多相位、多转变、多场景视频物体分割 (M3-VOS)，以验证模型对物体相位的理解能力。通过评估现有方法在处理相变物体时的表现，论文发现基于外观的方法在处理相变物体时存在显著改进空间，并提出了一个名为 ReVOS 的新模型，通过反向细化过程来提升性能。

链接: https://arxiv.org/abs/2412.13803
作者: Zixuan Chen,Jiaxin Li,Liming Tan,Yejie Guo,Junxuan Liang,Cewu Lu,Yonglu Li
机构: Shanghai Jiao Tong University(上海交通大学)
关键词: Intelligent robots, interact with diverse, Intelligent, phase transitions, objects
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Intelligent robots need to interact with diverse objects across various environments. The appearance and state of objects frequently undergo complex transformations depending on the object properties, e.g., phase transitions. However, in the vision community, segmenting dynamic objects with phase transitions is overlooked. In light of this, we introduce the concept of phase in segmentation, which categorizes real-world objects based on their visual characteristics and potential morphological and appearance changes. Then, we present a new benchmark, Multi-Phase, Multi-Transition, and Multi-Scenery Video Object Segmentation (M3-VOS), to verify the ability of models to understand object phases, which consists of 479 high-resolution videos spanning over 10 distinct everyday scenarios. It provides dense instance mask annotations that capture both object phases and their transitions. We evaluate state-of-the-art methods on M3-VOS, yielding several key insights. Notably, current appearance based approaches show significant room for improvement when handling objects with phase transitions. The inherent changes in disorder suggest that the predictive performance of the forward entropy-increasing process can be improved through a reverse entropy-reducing process. These findings lead us to propose ReVOS, a new plug-and-play model that improves its performance by reversal refinement. Our data and code will be publicly available
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[CV-42] An Efficient Occupancy World Model via Decoupled Dynamic Flow and Image-assisted Training

【速读】：该论文试图解决自动驾驶领域中4D场景预测问题，特别是如何高效地预测未来动态和静态物体的3D占用情况。解决方案的关键在于提出了DFIT-OccWorld模型，通过解耦动态流（decoupled dynamic flow）和图像辅助训练策略（image-assisted training strategy）来提升预测性能。具体来说，模型将占用预测问题重新表述为解耦的体素扭曲过程（decoupled voxels warping process），其中动态体素通过体素流（voxel flow）进行预测，而静态体素通过姿态变换（pose transformation）获取。此外，采用可微分体积渲染（differentiable volume rendering）生成深度图，并通过基于渲染的光度一致性（render-based photometric consistency）来增强预测的可靠性。这种方法在nuScenes和OpenScene基准测试中展示了最先进的性能，同时显著降低了计算成本。

链接: https://arxiv.org/abs/2412.13772
作者: Haiming Zhang,Ying Xue,Xu Yan,Jiacheng Zhang,Weichao Qiu,Dongfeng Bai,Bingbing Liu,Shuguang Cui,Zhen Li
机构: FNii, Shenzhen; SSE, CUHK-Shenzhen; HKU; Huawei Noah’s Ark Lab
关键词: predict potential future, potential future scenarios, future scenarios based, field of autonomous, autonomous driving
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:The field of autonomous driving is experiencing a surge of interest in world models, which aim to predict potential future scenarios based on historical observations. In this paper, we introduce DFIT-OccWorld, an efficient 3D occupancy world model that leverages decoupled dynamic flow and image-assisted training strategy, substantially improving 4D scene forecasting performance. To simplify the training process, we discard the previous two-stage training strategy and innovatively reformulate the occupancy forecasting problem as a decoupled voxels warping process. Our model forecasts future dynamic voxels by warping existing observations using voxel flow, whereas static voxels are easily obtained through pose transformation. Moreover, our method incorporates an image-assisted training paradigm to enhance prediction reliability. Specifically, differentiable volume rendering is adopted to generate rendered depth maps through predicted future volumes, which are adopted in render-based photometric consistency. Experiments demonstrate the effectiveness of our approach, showcasing its state-of-the-art performance on the nuScenes and OpenScene benchmarks for 4D occupancy forecasting, end-to-end motion planning and point cloud forecasting. Concretely, it achieves state-of-the-art performances compared to existing 3D world models while incurring substantially lower computational costs.
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[CV-43] Mesoscopic Insights: Orchestrating Multi-scale Hybrid Architecture for Image Manipulation Localization AAAI2025

【速读】：该论文试图解决图像篡改定位 (Image Manipulation Localization, IML) 中仅依赖微观（低级）痕迹的局限性问题，提出通过整合微观和宏观信息来构建介观层面的表示，从而提升IML的性能。解决方案的关键在于引入Mesorch架构，该架构通过并行结合Transformer和卷积神经网络 (CNN)，分别提取宏观语义信息和微观细节，并在不同尺度上无缝评估微观和宏观信息。此外，基于Mesorch架构，论文还提出了两个基线模型，用于通过介观表示解决IML任务，实验结果表明这些模型在性能、计算复杂性和鲁棒性方面均超越了当前的最先进方法。

链接: https://arxiv.org/abs/2412.13753
作者: Xuekang Zhu,Xiaochen Ma,Lei Su,Zhuohang Jiang,Bo Du,Xiwen Wang,Zeyu Lei,Wentao Feng,Chi-Man Pun,Jizhe Zhou
机构: Sichuan University (四川大学); Shenzhen University (深圳大学); Harbin Institute of Technology (哈尔滨工业大学)
关键词: addressing gaps overlooked, addressing gaps, gaps overlooked, mesoscopic level serves, IML
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: AAAI 2025. Code: $\href{ [this https URL](https://github.com/scu-zjz/Mesorch) }{this~url}$

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Abstract:The mesoscopic level serves as a bridge between the macroscopic and microscopic worlds, addressing gaps overlooked by both. Image manipulation localization (IML), a crucial technique to pursue truth from fake images, has long relied on low-level (microscopic-level) traces. However, in practice, most tampering aims to deceive the audience by altering image semantics. As a result, manipulation commonly occurs at the object level (macroscopic level), which is equally important as microscopic traces. Therefore, integrating these two levels into the mesoscopic level presents a new perspective for IML research. Inspired by this, our paper explores how to simultaneously construct mesoscopic representations of micro and macro information for IML and introduces the Mesorch architecture to orchestrate both. Specifically, this architecture i) combines Transformers and CNNs in parallel, with Transformers extracting macro information and CNNs capturing micro details, and ii) explores across different scales, assessing micro and macro information seamlessly. Additionally, based on the Mesorch architecture, the paper introduces two baseline models aimed at solving IML tasks through mesoscopic representation. Extensive experiments across four datasets have demonstrated that our models surpass the current state-of-the-art in terms of performance, computational complexity, and robustness.
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[CV-44] Multi-Exposure Image Fusion via Distilled 3D LUT Grid with Editable Mode

【速读】：该论文试图解决手持设备在高分辨率成像下，现有多曝光图像融合算法难以实时生成超高分辨率（UHD）高动态范围（HDR）图像的问题。解决方案的关键在于引入3D LUT技术，以在资源受限的设备上实现实时UHD图像增强。然而，由于多张不同曝光率图像的融合存在不确定性，这种不确定性会显著影响3D LUT网格的泛化能力。为此，论文提出采用教师-学生网络来建模3D LUT中的不确定性，并使用隐式表示函数提供可编辑的多曝光图像融合算法，以满足不同应用场景的需求。实验结果表明，该方法在效率和准确性上具有高度竞争力。

链接: https://arxiv.org/abs/2412.13749
作者: Xin Su,Zhuoran Zheng
机构: Fuzhou University(福州大学); Sun Yat-sen University(中山大学)
关键词: high dynamic range, rising imaging resolution, dynamic range image, fusion algorithms struggle, existing multi-exposure image
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:With the rising imaging resolution of handheld devices, existing multi-exposure image fusion algorithms struggle to generate a high dynamic range image with ultra-high resolution in real-time. Apart from that, there is a trend to design a manageable and editable algorithm as the different needs of real application scenarios. To tackle these issues, we introduce 3D LUT technology, which can enhance images with ultra-high-definition (UHD) resolution in real time on resource-constrained devices. However, since the fusion of information from multiple images with different exposure rates is uncertain, and this uncertainty significantly trials the generalization power of the 3D LUT grid. To address this issue and ensure a robust learning space for the model, we propose using a teacher-student network to model the uncertainty on the 3D LUT this http URL, we provide an editable mode for the multi-exposure image fusion algorithm by using the implicit representation function to match the requirements in different scenarios. Extensive experiments demonstrate that our proposed method is highly competitive in efficiency and accuracy.
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[CV-45] Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation

【速读】：该论文试图解决在医学图像分割中，由于标注数据有限导致的性能下降问题。解决方案的关键在于提出了一个可学习的提示引导的SAM诱导知识蒸馏框架（KnowSAM），通过多视角协同训练（Multi-view Co-training, MC）策略和可学习提示策略（Learnable Prompt Strategy, LPS）来动态生成密集提示并微调SAM模型，使其更适合医学图像分割任务。此外，论文还提出了SAM诱导知识蒸馏（SAM-induced Knowledge Distillation, SKD），将SAM的有用知识传递给两个子网络，帮助它们从SAM的预测中学习，并减轻训练过程中伪标签错误的影响。通过这些方法，模型在多种医学分割任务中表现优于现有的半监督分割方法，并且该框架可以无缝集成到其他半监督分割方法中以提升性能。

链接: https://arxiv.org/abs/2412.13742
作者: Kaiwen Huang,Tao Zhou,Huazhu Fu,Yizhe Zhang,Yi Zhou,Chen Gong,Dong Liang
机构: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; Institute of High-Performance Computing, Agency for Science, Technology and Research, Singapore; School of Computer Science and Engineering, Southeast University, Nanjing 211189, China; Lauterbur Research Center for Biomedical Imaging and the Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
关键词: medical image segmentation, medical image, image segmentation, segmentation, limited availability
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages, 7 figures

点击查看摘要

Abstract:The limited availability of labeled data has driven advancements in semi-supervised learning for medical image segmentation. Modern large-scale models tailored for general segmentation, such as the Segment Anything Model (SAM), have revealed robust generalization capabilities. However, applying these models directly to medical image segmentation still exposes performance degradation. In this paper, we propose a learnable prompting SAM-induced Knowledge distillation framework (KnowSAM) for semi-supervised medical image segmentation. Firstly, we propose a Multi-view Co-training (MC) strategy that employs two distinct sub-networks to employ a co-teaching paradigm, resulting in more robust outcomes. Secondly, we present a Learnable Prompt Strategy (LPS) to dynamically produce dense prompts and integrate an adapter to fine-tune SAM specifically for medical image segmentation tasks. Moreover, we propose SAM-induced Knowledge Distillation (SKD) to transfer useful knowledge from SAM to two sub-networks, enabling them to learn from SAM’s predictions and alleviate the effects of incorrect pseudo-labels during training. Notably, the predictions generated by our subnets are used to produce mask prompts for SAM, facilitating effective inter-module information exchange. Extensive experimental results on various medical segmentation tasks demonstrate that our model outperforms the state-of-the-art semi-supervised segmentation approaches. Crucially, our SAM distillation framework can be seamlessly integrated into other semi-supervised segmentation methods to enhance performance. The code will be released upon acceptance of this manuscript at: this https URL
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[CV-46] MedCoT: Medical Chain of Thought via Hierarchical Expert

【速读】：该论文试图解决医学视觉问答（Med-VQA）中存在的两个主要问题：一是现有研究过于关注答案的准确性，而忽视了推理路径和可解释性，这在临床环境中至关重要；二是当前的Med-VQA算法通常依赖单一模型，缺乏应对复杂医疗诊断所需的鲁棒性和多专家协作能力。解决方案的关键在于提出了MedCoT，一种新颖的分层专家验证推理链方法，旨在增强生物医学影像问答的解释性和准确性。MedCoT基于两个核心原则：明确推理路径的必要性和多专家审查以形成准确结论的需求。其方法包括初始专家提出诊断理由，后续专家验证这些理由，并通过本地部署的稀疏专家混合体进行投票达成共识，最终提供确切的诊断结果。

链接: https://arxiv.org/abs/2412.13736
作者: Jiaxiang Liu,Yuan Wang,Jiawei Du,Joey Tianyi Zhou,Zuozhu Liu
机构: ZJU-Angelalign R&D Center for Intelligence Healthcare, Zhejiang University, China; Centre for Frontier AI Research (CFAR), Agency for Science, Technology and Research (ASTAR), Singapore; Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (ASTAR), Singapore
关键词: Visual Question Answering, Medical Visual Question, Question Answering, Visual Question, Artificial intelligence
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Artificial intelligence has advanced in Medical Visual Question Answering (Med-VQA), but prevalent research tends to focus on the accuracy of the answers, often overlooking the reasoning paths and interpretability, which are crucial in clinical settings. Besides, current Med-VQA algorithms, typically reliant on singular models, lack the robustness needed for real-world medical diagnostics which usually require collaborative expert evaluation. To address these shortcomings, this paper presents MedCoT, a novel hierarchical expert verification reasoning chain method designed to enhance interpretability and accuracy in biomedical imaging inquiries. MedCoT is predicated on two principles: The necessity for explicit reasoning paths in Med-VQA and the requirement for multi-expert review to formulate accurate conclusions. The methodology involves an Initial Specialist proposing diagnostic rationales, followed by a Follow-up Specialist who validates these rationales, and finally, a consensus is reached through a vote among a sparse Mixture of Experts within the locally deployed Diagnostic Specialist, which then provides the definitive diagnosis. Experimental evaluations on four standard Med-VQA datasets demonstrate that MedCoT surpasses existing state-of-the-art approaches, providing significant improvements in performance and interpretability.
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[CV-47] 3D Registration in 30 Years: A Survey

【速读】：该论文试图解决3D点云配准（3D point cloud registration）这一计算机视觉、计算机图形学、机器人学、遥感等领域的基本问题。论文通过提供一个全面的综述，涵盖了从粗配准到精细配准、多视角配准、跨尺度配准以及多实例配准等多个子领域。解决方案的关键在于系统性地总结和分类现有的配准方法，详细讨论其优缺点，并提出未来研究方向的深刻见解。此外，论文还提供了数据集、评估指标和方法分类等全面信息，并通过一个定期更新的项目页面保持内容的时效性。

链接: https://arxiv.org/abs/2412.13735
作者: Jiaqi Yang,Chu’ai Zhang,Zhengbao Wang,Xinyue Cao,Xuan Ouyang,Xiyu Zhang,Zhenxuan Zeng,Zhao Zeng,Borui Lu,Zhiyi Xia,Qian Zhang,Yulan Guo,Yanning Zhang
机构: 未知
关键词: remote sensing, computer vision, computer graphics, point cloud registration, fundamental problem
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:3D point cloud registration is a fundamental problem in computer vision, computer graphics, robotics, remote sensing, and etc. Over the last thirty years, we have witnessed the amazing advancement in this area with numerous kinds of solutions. Although a handful of relevant surveys have been conducted, their coverage is still limited. In this work, we present a comprehensive survey on 3D point cloud registration, covering a set of sub-areas such as pairwise coarse registration, pairwise fine registration, multi-view registration, cross-scale registration, and multi-instance registration. The datasets, evaluation metrics, method taxonomy, discussions of the merits and demerits, insightful thoughts of future directions are comprehensively presented in this survey. The regularly updated project page of the survey is available at this https URL.
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[CV-48] xt2Relight: Creative Portrait Relighting with Text Guidance

【速读】：该论文试图解决文本驱动图像编辑模型在特定光照场景下泛化能力不足的问题。解决方案的关键在于引入了一种新颖的数据合成流程：首先，利用大型语言模型（如 ChatGPT）生成多样化的文本提示，描述具有各种光照的场景；然后，通过文本引导的图像生成模型创建与文本匹配的光照图像；接着，基于这些光照图像，使用单张肖像图像或光台系统捕获的 OLAT 图像进行基于图像的重光照，特别是背景重光照通过将光照图像表示为点光源集并转移到其他背景图像来实现；最后，生成式扩散模型通过辅助任务增强（如肖像优化和光源定位）学习合成的大规模数据，以关联潜在文本和光照分布，从而实现文本引导的肖像重光照。

链接: https://arxiv.org/abs/2412.13734
作者: Junuk Cha,Mengwei Ren,Krishna Kumar Singh,He Zhang,Yannick Hold-Geoffroy,Seunghyun Yoon,HyunJoon Jung,Jae Shin Yoon,Seungryul Baek
机构: 未知
关键词: text, lighting, present a lighting-aware, image, lighting-aware image editing
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:We present a lighting-aware image editing pipeline that, given a portrait image and a text prompt, performs single image relighting. Our model modifies the lighting and color of both the foreground and background to align with the provided text description. The unbounded nature in creativeness of a text allows us to describe the lighting of a scene with any sensory features including temperature, emotion, smell, time, and so on. However, the modeling of such mapping between the unbounded text and lighting is extremely challenging due to the lack of dataset where there exists no scalable data that provides large pairs of text and relighting, and therefore, current text-driven image editing models does not generalize to lighting-specific use cases. We overcome this problem by introducing a novel data synthesis pipeline: First, diverse and creative text prompts that describe the scenes with various lighting are automatically generated under a crafted hierarchy using a large language model (e.g., ChatGPT). A text-guided image generation model creates a lighting image that best matches the text. As a condition of the lighting images, we perform image-based relighting for both foreground and background using a single portrait image or a set of OLAT (One-Light-at-A-Time) images captured from lightstage system. Particularly for the background relighting, we represent the lighting image as a set of point lights and transfer them to other background images. A generative diffusion model learns the synthesized large-scale data with auxiliary task augmentation (e.g., portrait delighting and light positioning) to correlate the latent text and lighting distribution for text-guided portrait relighting.
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[CV-49] Modelling Multi-modal Cross-interaction for ML-FSIC Based on Local Feature Selection

【速读】：该论文旨在解决多标签少样本图像分类 (Multi-label Few-Shot Image Classification, ML-FSIC) 问题，即在每个标签仅有少量训练样本的情况下，为图像分配语义标签。其关键解决方案在于逐步精炼标签原型 (label prototypes)。首先，利用词嵌入 (word embeddings) 初始化原型，以利用标签的先验知识；其次，通过损失变化测量 (Loss Change Measurement, LCM) 策略从训练图像中选择最具代表性的局部特征；最后，利用多模态交叉交互机制 (multi-modal cross-interaction mechanism) 聚合这些局部特征，构建最终的标签原型。该方法在COCO、PASCAL VOC、NUS-WIDE和iMaterialist数据集上显著提升了当前的最先进水平。

链接: https://arxiv.org/abs/2412.13732
作者: Kun Yan,Zied Bouraoui,Fangyun Wei,Chang Xu,Ping Wang,Shoaib Jameel,Steven Schockaert
机构: 未知
关键词: few-shot image classification, assign semantic labels, multi-label few-shot image, assign semantic, small number
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted in Transactions on Multimedia Computing Communications and Applications

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Abstract:The aim of multi-label few-shot image classification (ML-FSIC) is to assign semantic labels to images, in settings where only a small number of training examples are available for each label. A key feature of the multi-label setting is that images often have several labels, which typically refer to objects appearing in different regions of the image. When estimating label prototypes, in a metric-based setting, it is thus important to determine which regions are relevant for which labels, but the limited amount of training data and the noisy nature of local features make this highly challenging. As a solution, we propose a strategy in which label prototypes are gradually refined. First, we initialize the prototypes using word embeddings, which allows us to leverage prior knowledge about the meaning of the labels. Second, taking advantage of these initial prototypes, we then use a Loss Change Measurement~(LCM) strategy to select the local features from the training images (i.e.\ the support set) that are most likely to be representative of a given label. Third, we construct the final prototype of the label by aggregating these representative local features using a multi-modal cross-interaction mechanism, which again relies on the initial word embedding-based prototypes. Experiments on COCO, PASCAL VOC, NUS-WIDE, and iMaterialist show that our model substantially improves the current state-of-the-art.
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[CV-50] Unified Understanding of Environment Task and Human for Human-Robot Interaction in Real-World Environments

【速读】：该论文旨在解决服务机器人在动态环境中执行人机交互任务（HRI）时面临的挑战，特别是如何适应动态环境、理解复杂任务并有效与人类沟通。解决方案的关键在于提出了一个综合系统，包括室内动态地图、任务理解系统和响应生成系统。室内动态地图通过分层管理占用网格地图和动态信息（如家具和人类）来优化机器人行为；任务理解系统通过预定义的任务流程来实现对多步骤任务的准确理解；响应生成系统则与任务理解并行运行，及时向人类传达机器人后续动作，以促进顺畅的HRI。实验结果表明，该系统在模拟餐厅环境中成功实现了与顾客的沟通和90%的点餐服务准确率，验证了其有效性。

链接: https://arxiv.org/abs/2412.13726
作者: Yuga Yano,Akinobu Mizutani,Yukiya Fukuda,Daiju Kanaoka,Tomohiro Ono,Hakaru Tamukoh
机构: Kyushu Institute of Technology(九州工业大学); Research Center for Neuromorphic AI Hardware(神经形态AI硬件研究中心)
关键词: HRI, HRI system, indoor dynamic map, system, understand the required
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
备注: 2024 33rd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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Abstract:To facilitate human–robot interaction (HRI) tasks in real-world scenarios, service robots must adapt to dynamic environments and understand the required tasks while effectively communicating with humans. To accomplish HRI in practice, we propose a novel indoor dynamic map, task understanding system, and response generation system. The indoor dynamic map optimizes robot behavior by managing an occupancy grid map and dynamic information, such as furniture and humans, in separate layers. The task understanding system targets tasks that require multiple actions, such as serving ordered items. Task representations that predefine the flow of necessary actions are applied to achieve highly accurate understanding. The response generation system is executed in parallel with task understanding to facilitate smooth HRI by informing humans of the subsequent actions of the robot. In this study, we focused on waiter duties in a restaurant setting as a representative application of HRI in a dynamic environment. We developed an HRI system that could perform tasks such as serving food and cleaning up while communicating with customers. In experiments conducted in a simulated restaurant environment, the proposed HRI system successfully communicated with customers and served ordered food with 90% accuracy. In a questionnaire administered after the experiment, the HRI system of the robot received 4.2 points out of 5. These outcomes indicated the effectiveness of the proposed method and HRI system in executing waiter tasks in real-world environments.
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[CV-51] Physics-Based Adversarial Attack on Near-Infrared Human Detector for Nighttime Surveillance Camera Systems ACM-MM2023

【速读】：该论文旨在解决近红外（NIR）图像理解中的基本漏洞问题，这些漏洞源于衣物反射特性和相机在NIR范围内的光谱敏感性导致的颜色和纹理丢失。论文的关键解决方案在于揭示了现有监控系统中光源与相机几乎共位配置的特性，使得在物理世界中可以进行隐蔽且完全被动的攻击。具体而言，论文展示了如何利用反光胶带和绝缘塑料胶带操控NIR图像的强度分布，并通过在数字空间中设计二值图案（通过黑盒查询和搜索）并将其物理实现为贴在衣物上的胶带，成功实施了对基于YOLO的人体检测器的攻击。这一研究强调了夜间监控系统在增强安全性方面的可靠性问题。

链接: https://arxiv.org/abs/2412.13709
作者: Muyao Niu,Zhuoxiao Li,Yifan Zhan,Huy H. Nguyen,Isao Echizen,Yinqiang Zheng
机构: The University of Tokyo; The University of Tokyo; The University of Tokyo; National Institute of Informatics; National Institute of Informatics; The University of Tokyo
关键词: nighttime modes based, surveillance cameras switch, illuminance levels, switch between daytime, modes based
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Appeared in ACM MM 2023

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Abstract:Many surveillance cameras switch between daytime and nighttime modes based on illuminance levels. During the day, the camera records ordinary RGB images through an enabled IR-cut filter. At night, the filter is disabled to capture near-infrared (NIR) light emitted from NIR LEDs typically mounted around the lens. While RGB-based AI algorithm vulnerabilities have been widely reported, the vulnerabilities of NIR-based AI have rarely been investigated. In this paper, we identify fundamental vulnerabilities in NIR-based image understanding caused by color and texture loss due to the intrinsic characteristics of clothes’ reflectance and cameras’ spectral sensitivity in the NIR range. We further show that the nearly co-located configuration of illuminants and cameras in existing surveillance systems facilitates concealing and fully passive attacks in the physical world. Specifically, we demonstrate how retro-reflective and insulation plastic tapes can manipulate the intensity distribution of NIR images. We showcase an attack on the YOLO-based human detector using binary patterns designed in the digital space (via black-box query and searching) and then physically realized using tapes pasted onto clothes. Our attack highlights significant reliability concerns for nighttime surveillance systems, which are intended to enhance security. Codes Available: this https URL
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[CV-52] JoVALE: Detecting Human Actions in Video Using Audiovisual and Language Contexts AAAI

【速读】：该论文试图解决视频动作检测 (Video Action Detection, VAD) 中如何有效利用多模态信息（包括音频、视觉线索和场景上下文）的问题。解决方案的关键在于提出了一个名为 Joint Actor-centric Visual, Audio, Language Encoder (JoVALE) 的新型多模态 VAD 架构，该架构首次将音频和视觉特征与从大规模图像描述模型中提取的场景描述上下文相结合。JoVALE 的核心原理是通过以演员为中心的方式聚合音频、视觉和场景描述上下文，识别并自适应地结合各模态中与动作相关的线索。论文还提出了一个专门的模块——Actor-centric Multi-modal Fusion Network，利用 Transformer 架构捕捉演员与多模态上下文之间的联合交互，从而显著提升了 VAD 的性能。

链接: https://arxiv.org/abs/2412.13708
作者: Taein Son,Soo Won Seo,Jisong Kim,Seok Hwan Lee,Jun Won Choi
机构: Seoul National University(首尔国立大学); Korea Advanced Institute of Science and Technology (KAIST)(韩国科学技术院)
关键词: Video Action Detection, categorizing action instances, Action Detection, categorizing action, action instances
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to AAAI Conference on Artificial Intelligence 2025, 9 pages, 5 figures

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Abstract:Video Action Detection (VAD) involves localizing and categorizing action instances in videos. Videos inherently contain various information sources, including audio, visual cues, and surrounding scene contexts. Effectively leveraging this multi-modal information for VAD is challenging, as the model must accurately focus on action-relevant cues. In this study, we introduce a novel multi-modal VAD architecture called the Joint Actor-centric Visual, Audio, Language Encoder (JoVALE). JoVALE is the first VAD method to integrate audio and visual features with scene descriptive context derived from large image captioning models. The core principle of JoVALE is the actor-centric aggregation of audio, visual, and scene descriptive contexts, where action-related cues from each modality are identified and adaptively combined. We propose a specialized module called the Actor-centric Multi-modal Fusion Network, designed to capture the joint interactions among actors and multi-modal contexts through Transformer architecture. Our evaluation conducted on three popular VAD benchmarks, AVA, UCF101-24, and JHMDB51-21, demonstrates that incorporating multi-modal information leads to significant performance gains. JoVALE achieves state-of-the-art performances. The code will be available at \textttthis https URL.
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[CV-53] Optical aberrations in autonomous driving: Physics-informed parameterized temperature scaling for neural network uncertainty calibration

【速读】：该论文试图解决在自动驾驶汽车感知系统中，由于挡风玻璃光学畸变引起的数据集偏移 (dataset shift) 问题，并提出了一种增强AI系统鲁棒性和可信度的解决方案。解决方案的关键在于通过双射映射 (bijective mapping) 将AI性能要求转化为光学指标，并利用光学系统的Zernike系数向量 (Zernike coefficient vector) 作为物理先验，将其引入神经网络校准架构中。这种方法显著降低了光学畸变情况下的平均预期校准误差 (mean expected calibration error)，从而提升了不确定性表示的可信度，并为感知系统的整体验证策略铺平了道路。

链接: https://arxiv.org/abs/2412.13695
作者: Dominik Werner Wolf,Alexander Braun,Markus Ulrich
机构: 未知
关键词: machine learning method, Huellermeier and Waegeman, learning method, key feature, machine learning
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Under review at the International Journal of Computer Vision (IJCV)

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Abstract:‘A trustworthy representation of uncertainty is desirable and should be considered as a key feature of any machine learning method’ (Huellermeier and Waegeman, 2021). This conclusion of Huellermeier et al. underpins the importance of calibrated uncertainties. Since AI-based algorithms are heavily impacted by dataset shifts, the automotive industry needs to safeguard its system against all possible contingencies. One important but often neglected dataset shift is caused by optical aberrations induced by the windshield. For the verification of the perception system performance, requirements on the AI performance need to be translated into optical metrics by a bijective mapping (Braun, 2023). Given this bijective mapping it is evident that the optical system characteristics add additional information about the magnitude of the dataset shift. As a consequence, we propose to incorporate a physical inductive bias into the neural network calibration architecture to enhance the robustness and the trustworthiness of the AI target application, which we demonstrate by using a semantic segmentation task as an example. By utilizing the Zernike coefficient vector of the optical system as a physical prior we can significantly reduce the mean expected calibration error in case of optical aberrations. As a result, we pave the way for a trustworthy uncertainty representation and for a holistic verification strategy of the perception chain.
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[CV-54] MMO-IG: Multi-Class and Multi-Scale Object Image Generation for Remote Sensing

【速读】：该论文试图解决现有深度生成模型（DGMs）在遥感图像（RS images）对象检测（RSIOD）研究中的局限性，即主要集中在全局布局视图下合成与真实图像对齐的遥感图像，限制了其在遥感图像对象检测中的应用。解决方案的关键在于提出了一个多类别和多尺度对象图像生成器（MMO-IG），通过同时从全局和局部视角生成带有监督对象标签的遥感图像。具体来说，MMO-IG利用等间距实例映射（ISIM）对不同遥感实例进行编码，并通过扩散模型的去噪过程解码每个实例区域，生成遥感图像。此外，构建了空间交叉依赖知识图（SCDKG）以确保多类别对象（MMOs）之间的真实和可靠的多向分布，减少源域和目标域之间的差异。通过结构化对象分布指令（SODI），结合基于SCDKG的ISIM，从全局视角指导合成遥感图像内容的生成。实验结果表明，MMO-IG在生成带有密集监督标签的遥感图像方面表现出色，并且使用MMO-IG预训练的遥感检测器在真实世界数据集上表现出优异的性能。

链接: https://arxiv.org/abs/2412.13684
作者: Chuang Yang,Bingxuan Zhao,Qing Zhou,Qi Wang
机构: Northwestern Polytechnical University(西北工业大学); Northwestern Polytechnical University(西北工业大学)
关键词: acquiring vast quantities, significantly advanced research, deep generative models, computer vision, providing a cost-effective
类目: Computer Vision and Pattern Recognition (cs.CV)
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点击查看摘要

Abstract:The rapid advancement of deep generative models (DGMs) has significantly advanced research in computer vision, providing a cost-effective alternative to acquiring vast quantities of expensive imagery. However, existing methods predominantly focus on synthesizing remote sensing (RS) images aligned with real images in a global layout view, which limits their applicability in RS image object detection (RSIOD) research. To address these challenges, we propose a multi-class and multi-scale object image generator based on DGMs, termed MMO-IG, designed to generate RS images with supervised object labels from global and local aspects simultaneously. Specifically, from the local view, MMO-IG encodes various RS instances using an iso-spacing instance map (ISIM). During the generation process, it decodes each instance region with iso-spacing value in ISIM-corresponding to both background and foreground instances-to produce RS images through the denoising process of diffusion models. Considering the complex interdependencies among MMOs, we construct a spatial-cross dependency knowledge graph (SCDKG). This ensures a realistic and reliable multidirectional distribution among MMOs for region embedding, thereby reducing the discrepancy between source and target domains. Besides, we propose a structured object distribution instruction (SODI) to guide the generation of synthesized RS image content from a global aspect with SCDKG-based ISIM together. Extensive experimental results demonstrate that our MMO-IG exhibits superior generation capabilities for RS images with dense MMO-supervised labels, and RS detectors pre-trained with MMO-IG show excellent performance on real-world datasets.
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[CV-55] When Should We Prefer State-to-Visual DAgger Over Visual Reinforcement Learning? AAAI AAAI2025

【速读】：该论文试图解决从高维视觉输入（如像素和点云）中学习策略的问题，特别是在视觉强化学习（Visual RL）中面临的样本效率和计算成本的挑战。解决方案的关键在于比较了两种方法：一种是两阶段的State-to-Visual DAgger框架，首先训练状态策略，然后通过在线模仿学习视觉策略；另一种是直接的视觉强化学习。研究通过在16个任务上进行实验，评估了这两种方法的渐近性能、样本效率和计算成本。结果表明，State-to-Visual DAgger在复杂任务中表现出更一致的性能，尽管在样本效率上的优势不明显，但通常能减少训练所需的总体时间。

链接: https://arxiv.org/abs/2412.13662
作者: Tongzhou Mu,Zhaoyang Li,Stanisław Wiktor Strzelecki,Xiu Yuan,Yunchao Yao,Litian Liang,Hao Su
机构: Tongzhou Mu1\equalcontrib, Zhaoyang Li1\equalcontrib, Stanisław Wiktor Strzelecki1\equalcontrib, Xiu Yuan1, Yunchao Yao1, Litian Liang1, Hao Su1
关键词: high-dimensional visual inputs, point clouds, pixels and point, visual, policies from high-dimensional
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
备注: Accepted by The 39th Annual AAAI Conference on Artificial Intelligence (AAAI 2025)

点击查看摘要

Abstract:Learning policies from high-dimensional visual inputs, such as pixels and point clouds, is crucial in various applications. Visual reinforcement learning is a promising approach that directly trains policies from visual observations, although it faces challenges in sample efficiency and computational costs. This study conducts an empirical comparison of State-to-Visual DAgger, a two-stage framework that initially trains a state policy before adopting online imitation to learn a visual policy, and Visual RL across a diverse set of tasks. We evaluate both methods across 16 tasks from three benchmarks, focusing on their asymptotic performance, sample efficiency, and computational costs. Surprisingly, our findings reveal that State-to-Visual DAgger does not universally outperform Visual RL but shows significant advantages in challenging tasks, offering more consistent performance. In contrast, its benefits in sample efficiency are less pronounced, although it often reduces the overall wall-clock time required for training. Based on our findings, we provide recommendations for practitioners and hope that our results contribute valuable perspectives for future research in visual policy learning.
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[CV-56] GLCF: A Global-Local Multimodal Coherence Analysis Framework for Talking Face Generation Detection

【速读】：该论文试图解决生成式对话人脸生成 (Talking Face Generation, TFG) 技术被滥用带来的社会风险问题，并填补该领域缺乏大规模公共数据集的空白。解决方案的关键在于构建了首个大规模多场景对话人脸数据集 (MSTF)，涵盖22种音视频伪造技术和11个生成场景，更贴近实际应用场景。此外，论文提出了一个TFG检测框架，通过全局和局部一致性分析，结合区域聚焦平滑检测模块 (RSFDM) 和差异捕捉时间帧聚合模块 (DCTAM) 来评估视频的全局时间一致性，并通过视觉-音频融合模块 (V-AFM) 评估局部时间视角下的视听一致性。实验结果表明，该方法在检测性能上优于现有的深度伪造检测技术。

链接: https://arxiv.org/abs/2412.13656
作者: Xiaocan Chen,Qilin Yin,Jiarui Liu,Wei Lu,Xiangyang Luo,Jiantao Zhou
机构: 未知
关键词: producing lifelike talking, lifelike talking videos, accompanying text, producing lifelike, facial images
类目: Computer Vision and Pattern Recognition (cs.CV)
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点击查看摘要

Abstract:Talking face generation (TFG) allows for producing lifelike talking videos of any character using only facial images and accompanying text. Abuse of this technology could pose significant risks to society, creating the urgent need for research into corresponding detection methods. However, research in this field has been hindered by the lack of public datasets. In this paper, we construct the first large-scale multi-scenario talking face dataset (MSTF), which contains 22 audio and video forgery techniques, filling the gap of datasets in this field. The dataset covers 11 generation scenarios and more than 20 semantic scenarios, closer to the practical application scenario of TFG. Besides, we also propose a TFG detection framework, which leverages the analysis of both global and local coherence in the multimodal content of TFG videos. Therefore, a region-focused smoothness detection module (RSFDM) and a discrepancy capture-time frame aggregation module (DCTAM) are introduced to evaluate the global temporal coherence of TFG videos, aggregating multi-grained spatial information. Additionally, a visual-audio fusion module (V-AFM) is designed to evaluate audiovisual coherence within a localized temporal perspective. Comprehensive experiments demonstrate the reasonableness and challenges of our datasets, while also indicating the superiority of our proposed method compared to the state-of-the-art deepfake detection approaches.
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[CV-57] VIIS: Visible and Infrared Information Synthesis for Severe Low-light Image Enhancement WACV2025

【速读】：该论文试图解决在极端低光条件下拍摄的图像中信息缺失的问题。现有的单一模态图像增强方法难以恢复缺乏有效信息的图像区域。论文提出了一种新的任务，称为可见光与红外信息合成 (Visible and Infrared Information Synthesis, VIIS)，旨在同时实现两种模态的信息增强与融合。解决方案的关键在于设计了一种基于图像增强的预训练任务 (Information Synthesis Pretext Task, ISPT)，并采用扩散模型框架，结合稀疏注意力机制的双模态残差 (Sparse Attention-based Dual-modalities Residual, SADMR) 条件机制，以增强两种模态之间的信息交互。该机制使两种模态的先验知识能够在去噪过程中自适应地迭代关注各自模态的信息，从而提升输出图像的感知质量。

链接: https://arxiv.org/abs/2412.13655
作者: Chen Zhao,Mengyuan Yu,Fan Yang,Peiguang Jing
机构: Tianjin University, Tianjin, China(天津大学，天津，中国); Southeast University, Nanjing, China(东南大学，南京，中国)
关键词: severe low-light circumstances, significant information absence, captured in severe, severe low-light, low-light circumstances
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to WACV 2025

点击查看摘要

Abstract:Images captured in severe low-light circumstances often suffer from significant information absence. Existing singular modality image enhancement methods struggle to restore image regions lacking valid information. By leveraging light-impervious infrared images, visible and infrared image fusion methods have the potential to reveal information hidden in darkness. However, they primarily emphasize inter-modal complementation but neglect intra-modal enhancement, limiting the perceptual quality of output images. To address these limitations, we propose a novel task, dubbed visible and infrared information synthesis (VIIS), which aims to achieve both information enhancement and fusion of the two modalities. Given the difficulty in obtaining ground truth in the VIIS task, we design an information synthesis pretext task (ISPT) based on image augmentation. We employ a diffusion model as the framework and design a sparse attention-based dual-modalities residual (SADMR) conditioning mechanism to enhance information interaction between the two modalities. This mechanism enables features with prior knowledge from both modalities to adaptively and iteratively attend to each modality’s information during the denoising process. Our extensive experiments demonstrate that our model qualitatively and quantitatively outperforms not only the state-of-the-art methods in relevant fields but also the newly designed baselines capable of both information enhancement and fusion. The code is available at this https URL.
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[CV-58] GAGS: Granularity-Aware Feature Distillation for Language Gaussian Splatting

【速读】：该论文试图解决三维开放词汇场景理解中的多视角不一致性问题，即在将二维CLIP特征蒸馏到三维高斯光栅化过程中，由于提取的二维特征在不同视角下不一致，导致对三维特征场的监督不稳定。解决方案的关键在于GAGS框架提出的两个创新策略：首先，GAGS通过将SAM的提示点密度与相机距离关联，显著提高了分割结果的多视角一致性；其次，GAGS引入了一个粒度因子来指导蒸馏过程，该因子可以在无监督的方式下学习，从而仅选择多视角一致的二维特征进行蒸馏。这些策略使得GAGS在视觉定位和语义分割任务中表现出显著的性能和稳定性提升，并且推理速度比基线方法快两倍。

链接: https://arxiv.org/abs/2412.13654
作者: Yuning Peng,Haiping Wang,Yuan Liu,Chenglu Wen,Zhen Dong,Bisheng Yang
机构: Wuhan University(武汉大学); Hong Kong University of Science and Technology(香港科技大学); Xiamen University(厦门大学); Nanyang Technological University(南洋理工大学)
关键词: accurately perceives complex, open-vocabulary scene understanding, perceives complex semantic, complex semantic properties, gained significant attention
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL

点击查看摘要

Abstract:3D open-vocabulary scene understanding, which accurately perceives complex semantic properties of objects in space, has gained significant attention in recent years. In this paper, we propose GAGS, a framework that distills 2D CLIP features into 3D Gaussian splatting, enabling open-vocabulary queries for renderings on arbitrary viewpoints. The main challenge of distilling 2D features for 3D fields lies in the multiview inconsistency of extracted 2D features, which provides unstable supervision for the 3D feature field. GAGS addresses this challenge with two novel strategies. First, GAGS associates the prompt point density of SAM with the camera distances, which significantly improves the multiview consistency of segmentation results. Second, GAGS further decodes a granularity factor to guide the distillation process and this granularity factor can be learned in a unsupervised manner to only select the multiview consistent 2D features in the distillation process. Experimental results on two datasets demonstrate significant performance and stability improvements of GAGS in visual grounding and semantic segmentation, with an inference speed 2 \times faster than baseline methods. The code and additional results are available at this https URL .
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[CV-59] RelationField: Relate Anything in Radiance Fields

【速读】：该论文试图解决当前神经辐射场（Neural Radiance Fields, NeRF）方法主要关注以物体为中心的表示，支持物体分割或检测，但在理解物体间语义关系方面仍未充分探索的问题。解决方案的关键在于提出RelationField，这是首个直接从神经辐射场中提取物体间关系的方法。RelationField通过将物体间的关系表示为神经辐射场中的一对光线，从而扩展了其公式以包含隐式关系查询。为了使RelationField能够理解复杂的开放词汇关系，关系知识从多模态大型语言模型（LLMs）中进行蒸馏。实验通过解决开放词汇的3D场景图生成任务和关系引导的实例分割任务，验证了RelationField的先进性能。

链接: https://arxiv.org/abs/2412.13652
作者: Sebastian Koch,Johanna Wald,Mirco Colosi,Narunas Vaskevicius,Pedro Hermosilla,Federico Tombari,Timo Ropinski
机构: University Ulm; Bosch Center for AI; Google; TU Vienna; TU Munich
关键词: Neural radiance fields, distilling open-vocabulary features, Neural radiance, vision-language models, learn features
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL

点击查看摘要

Abstract:Neural radiance fields are an emerging 3D scene representation and recently even been extended to learn features for scene understanding by distilling open-vocabulary features from vision-language models. However, current method primarily focus on object-centric representations, supporting object segmentation or detection, while understanding semantic relationships between objects remains largely unexplored. To address this gap, we propose RelationField, the first method to extract inter-object relationships directly from neural radiance fields. RelationField represents relationships between objects as pairs of rays within a neural radiance field, effectively extending its formulation to include implicit relationship queries. To teach RelationField complex, open-vocabulary relationships, relationship knowledge is distilled from multi-modal LLMs. To evaluate RelationField, we solve open-vocabulary 3D scene graph generation tasks and relationship-guided instance segmentation, achieving state-of-the-art performance in both tasks. See the project website at this https URL.
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[CV-60] Consistency of Compositional Generalization across Multiple Levels AAAI2025

【速读】：该论文试图解决模型在多层次新颖组合（novel compositions）上的组合泛化（compositional generalization）一致性问题。现有方法在组合泛化方面取得了一定成效，但在不同层次（如短语-短语、短语-词、词-词层次）上的泛化一致性尚未得到充分探索。论文提出的解决方案关键在于采用元学习（meta-learning）框架，通过逐步从简单到复杂的组合学习来实现一致性。具体方法是将原始训练集根据组合复杂度划分为多个验证集，并引入多个元权重网络（meta-weight-nets）为不同验证集中的样本生成权重，通过多层次优化方式独立且顺序地优化每个元权重网络的参数，以适应按组合复杂度递增的验证集。

链接: https://arxiv.org/abs/2412.13636
作者: Chuanhao Li,Zhen Li,Chenchen Jing,Xiaomeng Fan,Wenbo Ye,Yuwei Wu,Yunde Jia
机构: 1. School of Computer Science and Technology, Soochow University(苏州大学计算机科学与技术学院);
2. Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University(苏州大学功能纳米与软物质研究院);
3. School of Electronic and Information Engineering, Soochow University(苏州大学电子与信息工程学院)
关键词: Compositional generalization, multiple levels, Compositional, level, compositions
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted by AAAI 2025

点击查看摘要

Abstract:Compositional generalization is the capability of a model to understand novel compositions composed of seen concepts. There are multiple levels of novel compositions including phrase-phrase level, phrase-word level, and word-word level. Existing methods achieve promising compositional generalization, but the consistency of compositional generalization across multiple levels of novel compositions remains unexplored. The consistency refers to that a model should generalize to a phrase-phrase level novel composition, and phrase-word/word-word level novel compositions that can be derived from it simultaneously. In this paper, we propose a meta-learning based framework, for achieving consistent compositional generalization across multiple levels. The basic idea is to progressively learn compositions from simple to complex for consistency. Specifically, we divide the original training set into multiple validation sets based on compositional complexity, and introduce multiple meta-weight-nets to generate sample weights for samples in different validation sets. To fit the validation sets in order of increasing compositional complexity, we optimize the parameters of each meta-weight-net independently and sequentially in a multilevel optimization manner. We build a GQA-CCG dataset to quantitatively evaluate the consistency. Experimental results on visual question answering and temporal video grounding, demonstrate the effectiveness of the proposed framework. We release GQA-CCG at this https URL.
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[CV-61] Self-control: A Better Conditional Mechanism for Masked Autoregressive Model

【速读】：该论文试图解决现有自回归条件图像生成算法中，由于向量量化（vector quantization）的离散特性对生成图像质量的负面影响问题。解决方案的关键在于引入了一种新型的连续掩码自回归生成模型（continuous masked autoregressive model），并构建了一个自控网络（self-control network）。该网络通过自注意力机制（self-attention mechanism），将多模态条件信息（包括文本和图像）以串行方式整合到统一的序列中，从而实现对生成过程的条件控制，并有效避免了传统交叉注意力机制（cross-attention-based conditional fusion mechanism）带来的信息融合问题，提升了生成图像的质量和条件控制的灵活性。

链接: https://arxiv.org/abs/2412.13635
作者: Qiaoying Qu,Shiyu Shen
机构: IEEE Publication Technology Group(IEEE出版技术组)
关键词: image generation algorithms, autoregressive image generation, image generation, range of applications, generating photorealistic images
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Autoregressive conditional image generation algorithms are capable of generating photorealistic images that are consistent with given textual or image conditions, and have great potential for a wide range of applications. Nevertheless, the majority of popular autoregressive image generation methods rely heavily on vector quantization, and the inherent discrete characteristic of codebook presents a considerable challenge to achieving high-quality image generation. To address this limitation, this paper introduces a novel conditional introduction network for continuous masked autoregressive models. The proposed self-control network serves to mitigate the negative impact of vector quantization on the quality of the generated images, while simultaneously enhancing the conditional control during the generation process. In particular, the self-control network is constructed upon a continuous mask autoregressive generative model, which incorporates multimodal conditional information, including text and images, into a unified autoregressive sequence in a serial manner. Through a self-attention mechanism, the network is capable of generating images that are controllable based on specific conditions. The self-control network discards the conventional cross-attention-based conditional fusion mechanism and effectively unifies the conditional and generative information within the same space, thereby facilitating more seamless learning and fusion of multimodal features.
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[CV-62] MambaLCT: Boosting Tracking via Long-term Context State Space Model

【速读】：该论文试图解决视频序列中长期依赖的上下文信息构建不足的问题，现有方法仅考虑相邻帧或视频片段的对象信息，导致上下文信息利用不充分。解决方案的关键在于提出了一种名为MambaLCT的新方法，通过设计一个新颖的单向Context Mamba模块，沿时间维度扫描帧特征，从第一帧到当前帧收集目标变化线索，并将这些线索压缩到隐藏状态空间中，持续聚合目标变化信息。随后，将这些目标变化线索注入注意力机制，为模板帧和搜索帧之间的关系建模提供时间信息。MambaLCT的优势在于能够持续扩展上下文长度，捕捉完整的目标变化线索，从而增强跟踪器的稳定性和鲁棒性。

链接: https://arxiv.org/abs/2412.13615
作者: Xiaohai Li,Bineng Zhong,Qihua Liang,Guorong Li,Zhiyi Mo,Shuxiang Song
机构: 1. Guangdong Provincial Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, China (广东省计算科学重点实验室，中山大学，广州，中国); 2. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China (数据科学与计算机学院，中山大学，广州，中国); 3. School of Mathematics and Computational Science, Sun Yat-sen University, Guangzhou, China (数学与计算科学学院，中山大学，广州，中国)
关键词: Effectively constructing context, Effectively constructing, target change cues, constructing context information, target variation cues
类目: Computer Vision and Pattern Recognition (cs.CV)
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点击查看摘要

Abstract:Effectively constructing context information with long-term dependencies from video sequences is crucial for object tracking. However, the context length constructed by existing work is limited, only considering object information from adjacent frames or video clips, leading to insufficient utilization of contextual information. To address this issue, we propose MambaLCT, which constructs and utilizes target variation cues from the first frame to the current frame for robust tracking. First, a novel unidirectional Context Mamba module is designed to scan frame features along the temporal dimension, gathering target change cues throughout the entire sequence. Specifically, target-related information in frame features is compressed into a hidden state space through selective scanning mechanism. The target information across the entire video is continuously aggregated into target variation cues. Next, we inject the target change cues into the attention mechanism, providing temporal information for modeling the relationship between the template and search frames. The advantage of MambaLCT is its ability to continuously extend the length of the context, capturing complete target change cues, which enhances the stability and robustness of the tracker. Extensive experiments show that long-term context information enhances the model’s ability to perceive targets in complex scenarios. MambaLCT achieves new SOTA performance on six benchmarks while maintaining real-time running speeds.
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[CV-63] Robust Tracking via Mamba-based Context-aware Token Learning AAAI2025

【速读】：该论文试图解决现有跟踪方法在性能与计算成本之间难以平衡的问题，这些方法通常依赖于复杂且耗时的学习过程，通过输入更多图像或特征来结合时间和外观信息，从而增加了计算负担并引入了冗余和干扰信息。解决方案的关键在于提出了一种简单而稳健的跟踪器，该跟踪器将时间信息学习与外观建模分离，并通过一组代表性标记（tokens）而非多张图像或特征来提取时间关系。具体来说，论文引入了基于mamba的时间模块，通过滑动窗口内的自回归特性和交叉注意力机制，确保跟踪标记能够感知目标的外观变化和运动趋势，从而在保持实时速度的同时实现竞争性的性能。

链接: https://arxiv.org/abs/2412.13611
作者: Jinxia Xie,Bineng Zhong,Qihua Liang,Ning Li,Zhiyi Mo,Shuxiang Song
机构: 1. Guangdong Provincial Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, China(广东省计算科学重点实验室，中山大学，广州，中国); 2. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China(数据科学与计算机学院，中山大学，广州，中国); 3. School of Mathematics, Sun Yat-sen University, Guangzhou, China(数学学院，中山大学，广州，中国)
关键词: make a good, good trade-off, cost is crucial, track tokens, track
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: AAAI2025

点击查看摘要

Abstract:How to make a good trade-off between performance and computational cost is crucial for a tracker. However, current famous methods typically focus on complicated and time-consuming learning that combining temporal and appearance information by input more and more images (or features). Consequently, these methods not only increase the model’s computational source and learning burden but also introduce much useless and potentially interfering information. To alleviate the above issues, we propose a simple yet robust tracker that separates temporal information learning from appearance modeling and extracts temporal relations from a set of representative tokens rather than several images (or features). Specifically, we introduce one track token for each frame to collect the target’s appearance information in the backbone. Then, we design a mamba-based Temporal Module for track tokens to be aware of context by interacting with other track tokens within a sliding window. This module consists of a mamba layer with autoregressive characteristic and a cross-attention layer with strong global perception ability, ensuring sufficient interaction for track tokens to perceive the appearance changes and movement trends of the target. Finally, track tokens serve as a guidance to adjust the appearance feature for the final prediction in the head. Experiments show our method is effective and achieves competitive performance on multiple benchmarks at a real-time speed. Code and trained models will be available at this https URL.
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[CV-64] Faster and Stronger: When ANN-SNN Conversion Meets Parallel Spiking Calculation

【速读】：该论文试图解决Spiking Neural Network (SNN)在扩展到更大网络和复杂应用领域时面临的训练框架效率问题。现有的主要训练方法，如空间-时间反向传播 (Spatial-Temporal Back-propagation, STBP) 和ANN-SNN转换 (ANN-SNN Conversion)，存在显著的训练开销或推理延迟。论文提出的解决方案是一个新颖的并行转换学习框架，通过建立并行尖峰神经元在每个时间步与累积尖峰发射率之间的数学映射关系，实现了无损且具有排序特性的转换过程，并确定了每一步的最佳偏移距离。此外，通过结合分布感知误差校准技术，该框架能够高效地支持更广泛的激活函数，甚至在无需训练的情况下实现转换。实验结果表明，该方法在超低时间延迟下显著提升了转换性能，为SNN的监督训练提供了一种极具前景的途径。

链接: https://arxiv.org/abs/2412.13610
作者: Zecheng Hao,Zhaofei Yu,Tiejun Huang
机构: Peking University (北京大学); Institute for Artificial Intelligence, Peking University (北京大学人工智能研究所)
关键词: Spiking Neural Network, Neural Network, Spiking Neural, brain-inspired and energy-efficient, facing the pivotal
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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点击查看摘要

Abstract:Spiking Neural Network (SNN), as a brain-inspired and energy-efficient network, is currently facing the pivotal challenge of exploring a suitable and efficient learning framework. The predominant training methodologies, namely Spatial-Temporal Back-propagation (STBP) and ANN-SNN Conversion, are encumbered by substantial training overhead or pronounced inference latency, which impedes the advancement of SNNs in scaling to larger networks and navigating intricate application domains. In this work, we propose a novel parallel conversion learning framework, which establishes a mathematical mapping relationship between each time-step of the parallel spiking neurons and the cumulative spike firing rate. We theoretically validate the lossless and sorting properties of the conversion process, as well as pointing out the optimal shifting distance for each step. Furthermore, by integrating the above framework with the distribution-aware error calibration technique, we can achieve efficient conversion towards more general activation functions or training-free circumstance. Extensive experiments have confirmed the significant performance advantages of our method for various conversion cases under ultra-low time latency. To our best knowledge, this is the first work which jointly utilizes parallel spiking calculation and ANN-SNN Conversion, providing a highly promising approach for SNN supervised training.
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[CV-65] Sign-IDD: Iconicity Disentangled Diffusion for Sign Language Production

【速读】：该论文试图解决手语生成 (Sign Language Production, SLP) 中从文本词汇到手语姿势 (G2P) 转换的关键问题，特别是现有方法忽视了关节之间的相对位置关系。解决方案的关键在于提出了一个创新的图标性解耦扩散框架 (Sign-IDD)，通过引入图标性解耦 (Iconicity Disentanglement, ID) 模块，将传统的三维关节表示解耦为四维骨骼表示，包括三维空间方向向量和一维空间距离向量，从而更好地捕捉关节间的相对位置关系。此外，属性可控扩散 (Attribute Controllable Diffusion, ACD) 模块进一步约束关节关联，通过分离骨骼方向和长度属性，并利用这些属性指导姿势生成，从而提高生成姿势的准确性和自然度。

链接: https://arxiv.org/abs/2412.13609
作者: Shengeng Tang,Jiayi He,Dan Guo,Yanyan Wei,Feng Li,Richang Hong
机构: 未知
关键词: Sign Language Production, Language Production, semantically consistent sign, consistent sign videos, Sign Language
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注: 9 pages, 5 figures

点击查看摘要

Abstract:Sign Language Production (SLP) aims to generate semantically consistent sign videos from textual statements, where the conversion from textual glosses to sign poses (G2P) is a crucial step. Existing G2P methods typically treat sign poses as discrete three-dimensional coordinates and directly fit them, which overlooks the relative positional relationships among joints. To this end, we provide a new perspective, constraining joint associations and gesture details by modeling the limb bones to improve the accuracy and naturalness of the generated poses. In this work, we propose a pioneering iconicity disentangled diffusion framework, termed Sign-IDD, specifically designed for SLP. Sign-IDD incorporates a novel Iconicity Disentanglement (ID) module to bridge the gap between relative positions among joints. The ID module disentangles the conventional 3D joint representation into a 4D bone representation, comprising the 3D spatial direction vector and 1D spatial distance vector between adjacent joints. Additionally, an Attribute Controllable Diffusion (ACD) module is introduced to further constrain joint associations, in which the attribute separation layer aims to separate the bone direction and length attributes, and the attribute control layer is designed to guide the pose generation by leveraging the above attributes. The ACD module utilizes the gloss embeddings as semantic conditions and finally generates sign poses from noise embeddings. Extensive experiments on PHOENIX14T and USTC-CSL datasets validate the effectiveness of our method. The code is available at: this https URL.
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[CV-66] Hybrid CNN-LSTM based Indoor Pedestrian Localization with CSI Fingerprint Maps

【速读】：该论文试图解决基于Wi-Fi指纹的细粒度行人定位问题，关键在于提出了一种新颖的Wi-Fi指纹系统，利用信道状态信息（CSI）数据生成二维加通道的CSI指纹图（CSI Fingerprint Map）。该系统通过结合卷积神经网络（CNN）和长短期记忆循环神经网络（LSTM RNN）的混合架构，利用CSI数据中的频率多样性和空间多样性特征，捕捉邻近位置间的时间和空间关系信息，从而生成行人轨迹假设。随后，通过粒子滤波器筛选出最符合人类行走模型的轨迹假设。实验结果表明，该方法在动态和静态环境中均显著优于现有的深度学习定位方法，平均均方根误差（RMSE）分别为0.36米和0.17米，证明了在观测稀疏、基础设施要求有限以及训练和测试环境中存在适度噪声的情况下，基于Wi-Fi的细粒度行人定位具有可行性。

链接: https://arxiv.org/abs/2412.13601
作者: Muhammad Emad-ud-din
机构: 未知
关键词: CSI Fingerprint Map, Channel State Information, CSI data, Channel State, CSI Fingerprint
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 12 pages, 14 figures and 3 tables

点击查看摘要

Abstract:The paper presents a novel Wi-Fi fingerprinting system that uses Channel State Information (CSI) data for fine-grained pedestrian localization. The proposed system exploits the frequency diversity and spatial diversity of the features extracted from CSI data to generate a 2D+channel image termed as a CSI Fingerprint Map. We then use this CSI Fingerprint Map representation of CSI data to generate a pedestrian trajectory hypothesis using a hybrid architecture that combines a Convolutional Neural Network and a Long Short-Term Memory Recurrent Neural Network model. The proposed architecture exploits the temporal and spatial relationship information among the CSI data observations gathered at neighboring locations. A particle filter is then employed to separate out the most likely hypothesis matching a human walk model. The experimental performance of our method is compared to existing deep learning localization methods such ConFi, DeepFi and to a self-developed temporal-feature based LSTM based location classifier. The experimental results show marked improvement with an average RMSE of 0.36 m in a moderately dynamic and 0.17 m in a static environment. Our method is essentially a proof of concept that with (1) sparse availability of observations, (2) limited infrastructure requirements, (3) moderate level of short-term and long-term noise in the training and testing environment, reliable fine-grained Wi-Fi based pedestrian localization is a potential option.
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[CV-67] Generalizable Sensor-Based Activity Recognition via Categorical Concept Invariant Learning AAAI2025

【速读】：该论文试图解决人类活动识别 (Human Activity Recognition, HAR) 中由于跨受试者变异性（如年龄、性别、行为习惯等）导致的测试集与训练集分布不一致的问题，从而影响模型的泛化性能。解决方案的关键在于提出了一种分类概念不变学习 (Categorical Concept Invariant Learning, CCIL) 框架，通过引入概念矩阵在训练阶段同时关注特征不变性和逻辑不变性，确保属于同一活动类别的样本具有相似的概念矩阵，从而提升模型在跨人、跨数据集、跨位置以及一人到另一人等不同场景下的泛化能力。

链接: https://arxiv.org/abs/2412.13594
作者: Di Xiong,Shuoyuan Wang,Lei Zhang,Wenbo Huang,Chaolei Han
机构: 1. School of Computer Science and Technology, Soochow University, Suzhou, China(苏州大学计算机科学与技术学院，苏州，中国);
2. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China(南京理工大学计算机科学与工程学院，南京，中国);
3. School of Computer Science and Engineering, Southeast University, Nanjing, China(东南大学计算机科学与工程学院，南京，中国)
关键词: massive sensor data, Human Activity Recognition, Human Activity, aims to recognize, sensor data
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted by AAAI 2025

点击查看摘要

Abstract:Human Activity Recognition (HAR) aims to recognize activities by training models on massive sensor data. In real-world deployment, a crucial aspect of HAR that has been largely overlooked is that the test sets may have different distributions from training sets due to inter-subject variability including age, gender, behavioral habits, etc., which leads to poor generalization performance. One promising solution is to learn domain-invariant representations to enable a model to generalize on an unseen distribution. However, most existing methods only consider the feature-invariance of the penultimate layer for domain-invariant learning, which leads to suboptimal results. In this paper, we propose a Categorical Concept Invariant Learning (CCIL) framework for generalizable activity recognition, which introduces a concept matrix to regularize the model in the training stage by simultaneously concentrating on feature-invariance and logit-invariance. Our key idea is that the concept matrix for samples belonging to the same activity category should be similar. Extensive experiments on four public HAR benchmarks demonstrate that our CCIL substantially outperforms the state-of-the-art approaches under cross-person, cross-dataset, cross-position, and one-person-to-another settings.
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[CV-68] Bridge then Begin Anew: Generating Target-relevant Intermediate Model for Source-free Visual Emotion Adaptation AAAI2025

【速读】：该论文试图解决视觉情感识别 (Visual Emotion Recognition, VER) 领域中由于情感的主观性和模糊性导致的可靠大规模数据标注困难问题，特别是在源数据因隐私问题不可访问的情况下。解决方案的关键是提出了一种新的任务：无源域自适应 (Source-Free Domain Adaptation, SFDA)，并通过名为“Bridge then Begin Anew (BBA)”的框架实现。该框架包括两个步骤：域桥接模型生成 (Domain-Bridged Model Generation, DMG) 和目标相关模型适应 (Target-Related Model Adaptation, TMA)。DMG通过生成一个中间模型来弥合跨域差距，避免直接对齐差异显著的VER数据集；TMA则重新训练目标模型以适应目标结构，避免源特定知识的干扰。实验结果表明，BBA在多个SFDA设置中显著优于现有的SFDA方法和无监督域适应方法。

链接: https://arxiv.org/abs/2412.13577
作者: Jiankun Zhu,Sicheng Zhao,Jing Jiang,Wenbo Tang,Zhaopan Xu,Tingting Han,Pengfei Xu,Hongxun Yao
机构: 1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China(哈尔滨工业大学计算机科学与技术学院，哈尔滨，中国);
2. School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China(哈尔滨工业大学计算机科学与技术学院，深圳，中国);
3. School of Computer Science and Technology, Harbin Institute of Technology, Weihai, China(哈尔滨工业大学计算机科学与技术学院，威海，中国);
4. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China(哈尔滨工业大学计算机科学与技术学院，哈尔滨，中国)
关键词: Visual emotion recognition, attracted increasing attention, visual stimuli, understanding humans’ emotional, humans’ emotional reactions
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted by AAAI2025

点击查看摘要

Abstract:Visual emotion recognition (VER), which aims at understanding humans’ emotional reactions toward different visual stimuli, has attracted increasing attention. Given the subjective and ambiguous characteristics of emotion, annotating a reliable large-scale dataset is hard. For reducing reliance on data labeling, domain adaptation offers an alternative solution by adapting models trained on labeled source data to unlabeled target data. Conventional domain adaptation methods require access to source data. However, due to privacy concerns, source emotional data may be inaccessible. To address this issue, we propose an unexplored task: source-free domain adaptation (SFDA) for VER, which does not have access to source data during the adaptation process. To achieve this, we propose a novel framework termed Bridge then Begin Anew (BBA), which consists of two steps: domain-bridged model generation (DMG) and target-related model adaptation (TMA). First, the DMG bridges cross-domain gaps by generating an intermediate model, avoiding direct alignment between two VER datasets with significant differences. Then, the TMA begins training the target model anew to fit the target structure, avoiding the influence of source-specific knowledge. Extensive experiments are conducted on six SFDA settings for VER. The results demonstrate the effectiveness of BBA, which achieves remarkable performance gains compared with state-of-the-art SFDA methods and outperforms representative unsupervised domain adaptation approaches.
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[CV-69] Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes

【速读】：该论文试图解决领域泛化（Domain Generalization）中模型在不同训练域中未能同时达到最优平坦最小值（flat minima）的问题，从而限制了模型在未见测试域上的泛化能力。解决方案的关键在于提出了一种迭代自反馈训练（Self-Feedback Training, SFT）框架，通过逐步优化不同域的损失景观（loss landscapes）的一致性，寻找跨域共享的平坦最小值。SFT通过交替生成反馈信号来衡量不同域损失景观的不一致性，并利用该信号来精炼损失景观，从而提高平坦最小值的一致性，进而增强模型的跨域泛化能力。

链接: https://arxiv.org/abs/2412.13573
作者: Aodi Li,Liansheng Zhuang,Xiao Long,Minghong Yao,Shafei Wang
机构: University of Science and Technology of China (中国科学技术大学); Peng Cheng Laboratory (鹏城实验室)
关键词: unseen test domains, loss landscapes, multiple training domains, flat minima, Domain generalization aims
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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点击查看摘要

Abstract:Domain generalization aims to learn a model from multiple training domains and generalize it to unseen test domains. Recent theory has shown that seeking the deep models, whose parameters lie in the flat minima of the loss landscape, can significantly reduce the out-of-domain generalization error. However, existing methods often neglect the consistency of loss landscapes in different domains, resulting in models that are not simultaneously in the optimal flat minima in all domains, which limits their generalization ability. To address this issue, this paper proposes an iterative Self-Feedback Training (SFT) framework to seek consistent flat minima that are shared across different domains by progressively refining loss landscapes during training. It alternatively generates a feedback signal by measuring the inconsistency of loss landscapes in different domains and refines these loss landscapes for greater consistency using this feedback signal. Benefiting from the consistency of the flat minima within these refined loss landscapes, our SFT helps achieve better out-of-domain generalization. Extensive experiments on DomainBed demonstrate superior performances of SFT when compared to state-of-the-art sharpness-aware methods and other prevalent DG baselines. On average across five DG benchmarks, SFT surpasses the sharpness-aware minimization by 2.6% with ResNet-50 and 1.5% with ViT-B/16, respectively. The code will be available soon.
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[CV-70] Multi-View Pedestrian Occupancy Prediction with a Novel Synthetic Dataset AAAI2025

【速读】：该论文试图解决在城市交通中预测行人占用率的高级挑战，作为多视角行人检测的扩展。解决方案的关键在于创建了一个新的合成数据集MVP-Occ，该数据集针对大规模场景中的密集行人场景，使用体素结构提供详细的行人表示，并伴随丰富的语义场景理解标签，以促进视觉导航和行人空间信息的洞察。此外，论文提出了一个名为OmniOcc的稳健基线模型，该模型能够从多视角图像中预测整个场景的体素占用状态和全景标签，通过深入分析评估了模型关键元素的贡献和重要性。

链接: https://arxiv.org/abs/2412.13569
作者: Sithu Aung,Min-Cheol Sagong,Junghyun Cho
机构: Korea Advanced Institute of Science and Technology (KAIST)(韩国科学技术院); Korea Institute of Science and Technology (KIST)(韩国科学技术院); Korea University (高丽大学)
关键词: urban traffic, address an advanced, advanced challenge, detection in urban, multi-view pedestrian detection
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: AAAI 2025

点击查看摘要

Abstract:We address an advanced challenge of predicting pedestrian occupancy as an extension of multi-view pedestrian detection in urban traffic. To support this, we have created a new synthetic dataset called MVP-Occ, designed for dense pedestrian scenarios in large-scale scenes. Our dataset provides detailed representations of pedestrians using voxel structures, accompanied by rich semantic scene understanding labels, facilitating visual navigation and insights into pedestrian spatial information. Furthermore, we present a robust baseline model, termed OmniOcc, capable of predicting both the voxel occupancy state and panoptic labels for the entire scene from multi-view images. Through in-depth analysis, we identify and evaluate the key elements of our proposed model, highlighting their specific contributions and importance.
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[CV-71] CA-Edit: Causality-Aware Condition Adapter for High-Fidelity Local Facial Attribute Editing AAAI

【速读】：该论文试图解决现有面部属性编辑方法在局部编辑时出现的两个主要问题：一是需要针对不同编辑效果进行额外微调，二是容易影响编辑区域以外的部分。现有的修复方法虽然可以在编辑目标区域时保留外部区域，但仍存在生成结果与面部属性描述不一致以及面部皮肤细节丢失的问题。论文提出的解决方案包括：(i) 引入一种新的数据利用策略，从数据驱动的角度构建包含属性-文本-图像三元组的数据集；(ii) 提出因果感知条件适配器（Causality-Aware Condition Adapter），增强特定细节的上下文因果关系建模，同时编码原始图像中的皮肤细节并防止这些细节与文本条件之间的冲突；(iii) 引入皮肤过渡频率引导技术（Skin Transition Frequency Guidance），通过低频对齐的采样引导实现上下文因果关系的局部建模。这些方法共同提升了局部属性编辑的保真度和可编辑性。

链接: https://arxiv.org/abs/2412.13565
作者: Xiaole Xian,Xilin He,Zenghao Niu,Junliang Zhang,Weicheng Xie,Siyang Song,Zitong Yu,Linlin Shen
机构: 未知
关键词: require additional fine-tuning, high-fidelity local facial, existing editing methods, efficient and high-fidelity, require additional
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: accepted by aaai

点击查看摘要

Abstract:For efficient and high-fidelity local facial attribute editing, most existing editing methods either require additional fine-tuning for different editing effects or tend to affect beyond the editing regions. Alternatively, inpainting methods can edit the target image region while preserving external areas. However, current inpainting methods still suffer from the generation misalignment with facial attributes description and the loss of facial skin details. To address these challenges, (i) a novel data utilization strategy is introduced to construct datasets consisting of attribute-text-image triples from a data-driven perspective, (ii) a Causality-Aware Condition Adapter is proposed to enhance the contextual causality modeling of specific details, which encodes the skin details from the original image while preventing conflicts between these cues and textual conditions. In addition, a Skin Transition Frequency Guidance technique is introduced for the local modeling of contextual causality via sampling guidance driven by low-frequency alignment. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our method in boosting both fidelity and editability for localized attribute editing. The code is available at this https URL.
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[CV-72] DragScene: Interactive 3D Scene Editing with Single-view Drag Instructions

【速读】：该论文试图解决3D场景编辑中多视角一致性问题，特别是在基于拖拽式编辑（drag-style editing）时如何实现局部化、直观化的编辑效果。解决方案的关键在于引入DragScene框架，通过在参考视图上进行潜在优化（latent optimization）生成2D编辑，并利用点云表示（point-based representation）重建粗略的3D线索，将编辑后的视图潜在表示映射到这些3D线索上，从而指导其他视图的潜在优化，确保编辑在多视角间无缝传播并保持一致性。最终，从编辑后的多视角图像中重建目标3D场景。

链接: https://arxiv.org/abs/2412.13552
作者: Chenghao Gu,Zhenzhe Li,Zhengqi Zhang,Yunpeng Bai,Shuzhao Xie,Zhi Wang
机构: Shenzhen International Graduate School, Tsinghua University(深圳国际研究生院，清华大学); College of Artificial Intelligence, Xi’an Jiaotong University(人工智能学院，西安交通大学); School of Software, Beihang University(软件学院，北京航空航天大学); Department of Computer Science, The University of Texas at Austin(德克萨斯大学奥斯汀分校计算机科学系)
关键词: shown remarkable capability, editing, shown remarkable, Drag-style editing, remarkable capability
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注:

点击查看摘要

Abstract:3D editing has shown remarkable capability in editing scenes based on various instructions. However, existing methods struggle with achieving intuitive, localized editing, such as selectively making flowers blossom. Drag-style editing has shown exceptional capability to edit images with direct manipulation instead of ambiguous text commands. Nevertheless, extending drag-based editing to 3D scenes presents substantial challenges due to multi-view inconsistency. To this end, we introduce DragScene, a framework that integrates drag-style editing with diverse 3D representations. First, latent optimization is performed on a reference view to generate 2D edits based on user instructions. Subsequently, coarse 3D clues are reconstructed from the reference view using a point-based representation to capture the geometric details of the edits. The latent representation of the edited view is then mapped to these 3D clues, guiding the latent optimization of other views. This process ensures that edits are propagated seamlessly across multiple views, maintaining multi-view consistency. Finally, the target 3D scene is reconstructed from the edited multi-view images. Extensive experiments demonstrate that DragScene facilitates precise and flexible drag-style editing of 3D scenes, supporting broad applicability across diverse 3D representations.
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[CV-73] urbo-GS: Accelerating 3D Gaussian Fitting for High-Quality Radiance Fields

【速读】：该论文试图解决3D高斯喷射 (3D Gaussian Splatting, 3DGS) 模型在训练时间过长的问题，尤其是在处理200个视图的场景时，训练时间通常需要30分钟。解决方案的关键在于通过减少优化步骤来加速训练过程，同时保持高质量的新视图渲染。具体措施包括结合位置误差和外观误差的指导来实现更有效的密集化，开发收敛感知预算控制机制以平衡新旧高斯函数的添加和拟合，以及从频繁访问的区域中选择性地添加新高斯函数。此外，引入基于膨胀的渲染技术以加速4K分辨率图像的快速拟合。最终，Turbo-GS方法将优化步骤减少到原有方法的三分之一，同时保持或提升了新视图渲染质量，显著加快了优化速度。

链接: https://arxiv.org/abs/2412.13547
作者: Tao Lu,Ankit Dhiman,R Srinath,Emre Arslan,Angela Xing,Yuanbo Xiangli,R Venkatesh Babu,Srinath Sridhar
机构: Brown University (布朗大学); Indian Institute of Science, Bangalore (印度科学学院，班加罗尔); Cornell University (康奈尔大学)
关键词: Novel-view synthesis, mixed reality, important problem, problem in computer, computer vision
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL

点击查看摘要

Abstract:Novel-view synthesis is an important problem in computer vision with applications in 3D reconstruction, mixed reality, and robotics. Recent methods like 3D Gaussian Splatting (3DGS) have become the preferred method for this task, providing high-quality novel views in real time. However, the training time of a 3DGS model is slow, often taking 30 minutes for a scene with 200 views. In contrast, our goal is to reduce the optimization time by training for fewer steps while maintaining high rendering quality. Specifically, we combine the guidance from both the position error and the appearance error to achieve a more effective densification. To balance the rate between adding new Gaussians and fitting old Gaussians, we develop a convergence-aware budget control mechanism. Moreover, to make the densification process more reliable, we selectively add new Gaussians from mostly visited regions. With these designs, we reduce the Gaussian optimization steps to one-third of the previous approach while achieving a comparable or even better novel view rendering quality. To further facilitate the rapid fitting of 4K resolution images, we introduce a dilation-based rendering technique. Our method, Turbo-GS, speeds up optimization for typical scenes and scales well to high-resolution (4K) scenarios on standard datasets. Through extensive experiments, we show that our method is significantly faster in optimization than other methods while retaining quality. Project page: this https URL.
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[CV-74] Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition

【速读】：该论文试图解决细粒度情感识别 (Fine-grained Emotion Recognition, FER) 在实际应用中的三个关键挑战：(i) 依赖大量连续标注数据导致的高成本和时间消耗；(ii) 无法捕捉情感模式变化引起的时间异质性；(iii) 未考虑不同FER场景中的空间异质性。解决方案的关键在于提出了时空模糊导向的多模态元学习框架 (Spatio-Temporal Fuzzy-oriented Multi-modal Meta-learning framework, ST-F2M)。该框架通过将多模态视频分割为多个视图，利用空间和时间卷积模块编码数据，并引入模糊语义信息处理情感的复杂性和模糊性，最终通过元循环神经网络学习情感相关的通用元知识，从而实现快速且鲁棒的细粒度情感识别。

链接: https://arxiv.org/abs/2412.13541
作者: Jingyao Wang,Yuxuan Yang,Wenwen Qiang,Changwen Zheng,Hui Xiong
机构: University of Chinese Academy of Sciences (中国科学院大学); National Key Laboratory of Space Integrated Information System, Institute of Software Chinese Academy of Sciences (中国科学院软件研究所空间综合信息系统国家重点实验室); Suzhou University of Science and Technology (苏州科技大学); Hong Kong University of Science and Technology (香港科技大学)
关键词: personalized recommendations, plays a vital, disease diagnosis, multimedia mining, Fuzzy-oriented Multi-modal Meta-learning
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
备注: 13 pages, Submitted to TMM in 30-May-2024

点击查看摘要

Abstract:Fine-grained emotion recognition (FER) plays a vital role in various fields, such as disease diagnosis, personalized recommendations, and multimedia mining. However, existing FER methods face three key challenges in real-world applications: (i) they rely on large amounts of continuously annotated data to ensure accuracy since emotions are complex and ambiguous in reality, which is costly and time-consuming; (ii) they cannot capture the temporal heterogeneity caused by changing emotion patterns, because they usually assume that the temporal correlation within sampling periods is the same; (iii) they do not consider the spatial heterogeneity of different FER scenarios, that is, the distribution of emotion information in different data may have bias or interference. To address these challenges, we propose a Spatio-Temporal Fuzzy-oriented Multi-modal Meta-learning framework (ST-F2M). Specifically, ST-F2M first divides the multi-modal videos into multiple views, and each view corresponds to one modality of one emotion. Multiple randomly selected views for the same emotion form a meta-training task. Next, ST-F2M uses an integrated module with spatial and temporal convolutions to encode the data of each task, reflecting the spatial and temporal heterogeneity. Then it adds fuzzy semantic information to each task based on generalized fuzzy rules, which helps handle the complexity and ambiguity of emotions. Finally, ST-F2M learns emotion-related general meta-knowledge through meta-recurrent neural networks to achieve fast and robust fine-grained emotion recognition. Extensive experiments show that ST-F2M outperforms various state-of-the-art methods in terms of accuracy and model efficiency. In addition, we construct ablation studies and further analysis to explore why ST-F2M performs well.
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[CV-75] Language-guided Medical Image Segmentation with Target-informed Multi-level Contrastive Alignments

【速读】：该论文试图解决语言引导分割方法中图像与文本模态之间的模式差异问题，特别是在医疗图像分割中，现有方法未能有效融合低层次的局部图像细节和文本信息，导致特征对齐不充分。解决方案的关键在于提出了目标信息引导的多层次对比对齐方法 (Target-informed Multi-level Contrastive Alignments, TMCA)，通过引入目标敏感的语义距离模块和多层次对齐策略，实现了细粒度的图像-文本对齐，并利用语言引导的目标增强模块来聚焦于关键的局部图像特征。这一方法在多个医疗图像数据集上展示了优越的性能。

链接: https://arxiv.org/abs/2412.13533
作者: Mingjian Li,Mingyuan Meng,Shuchang Ye,David Dagan Feng,Lei Bi,Jinman Kim
机构: 未知
关键词: language-guided segmentation, crucial in modern, aid into diagnosis, image, segmentation
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Medical image segmentation is crucial in modern medical image analysis, which can aid into diagnosis of various disease conditions. Recently, language-guided segmentation methods have shown promising results in automating image segmentation where text reports are incorporated as guidance. These text reports, containing image impressions and insights given by clinicians, provides auxiliary guidance. However, these methods neglect the inherent pattern gaps between the two distinct modalities, which leads to sub-optimal image-text feature fusion without proper cross-modality feature alignments. Contrastive alignments are widely used to associate image-text semantics in representation learning; however, it has not been exploited to bridge the pattern gaps in language-guided segmentation that relies on subtle low level image details to represent diseases. Existing contrastive alignment methods typically algin high-level global image semantics without involving low-level, localized target information, and therefore fails to explore fine-grained text guidance for language-guided segmentation. In this study, we propose a language-guided segmentation network with Target-informed Multi-level Contrastive Alignments (TMCA). TMCA enables target-informed cross-modality alignments and fine-grained text guidance to bridge the pattern gaps in language-guided segmentation. Specifically, we introduce: 1) a target-sensitive semantic distance module that enables granular image-text alignment modelling, and 2) a multi-level alignment strategy that directs text guidance on low-level image features. In addition, a language-guided target enhancement module is proposed to leverage the aligned text to redirect attention to focus on critical localized image features. Extensive experiments on 4 image-text datasets, involving 3 medical imaging modalities, demonstrated that our TMCA achieved superior performances.
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[CV-76] Hybrid Data-Free Knowledge Distillation

【速读】：该论文试图解决在无数据知识蒸馏（Data-free Knowledge Distillation）中，现有基于收集和生成的方法在实际场景中因难以获取或模拟足够真实数据而表现不佳的问题。解决方案的关键是提出了一种混合数据无蒸馏方法（Hybrid Data-Free Distillation, HiDFD），该方法结合了少量收集的真实数据和生成的合成数据来训练学生网络。具体而言，HiDFD包含两个核心模块：教师引导的生成模块和学生蒸馏模块。教师引导的生成模块通过教师网络指导生成对抗网络（GAN）生成高质量的合成样本，并设计了特征集成机制和类别频率平滑技术来防止过拟合和平衡生成训练。学生蒸馏模块则通过数据膨胀策略和基于分类器共享的特征对齐技术，有效利用真实和合成数据的混合来训练学生网络。实验结果表明，HiDFD在仅使用现有方法1/120的收集数据量的情况下，达到了最先进的性能。

链接: https://arxiv.org/abs/2412.13525
作者: Jialiang Tang,Shuo Chen,Chen Gong
机构: 1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China(哈尔滨工业大学计算机科学与技术学院，哈尔滨，中国);
2. Key Laboratory of Computer Networks and Information Security, Ministry of Industry and Information Technology, Harbin, China(工业和信息化部计算机网络与信息安全重点实验室，哈尔滨，中国);
3. Collaborative Innovation Center of Information Technology, Harbin, China(信息技术协同创新中心，哈尔滨，中国);
4. Department of Computer Science, University of California, Los Angeles, USA(加州大学洛杉矶分校计算机科学系，美国);
5. Department of Electrical and Computer Engineering, University of California, Los Angeles, USA(加州大学洛杉矶分校电气与计算机工程系，美国)
关键词: Data-free knowledge distillation, Data-free knowledge, pre-trained large teacher, teacher network, knowledge distillation aims
类目: Computer Vision and Pattern Recognition (cs.CV)
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点击查看摘要

Abstract:Data-free knowledge distillation aims to learn a compact student network from a pre-trained large teacher network without using the original training data of the teacher network. Existing collection-based and generation-based methods train student networks by collecting massive real examples and generating synthetic examples, respectively. However, they inevitably become weak in practical scenarios due to the difficulties in gathering or emulating sufficient real-world data. To solve this problem, we propose a novel method called \textbfHybr\textbfid \textbfData-\textbfFree \textbfDistillation (HiDFD), which leverages only a small amount of collected data as well as generates sufficient examples for training student networks. Our HiDFD comprises two primary modules, \textiti.e., the teacher-guided generation and student distillation. The teacher-guided generation module guides a Generative Adversarial Network (GAN) by the teacher network to produce high-quality synthetic examples from very few real-world collected examples. Specifically, we design a feature integration mechanism to prevent the GAN from overfitting and facilitate the reliable representation learning from the teacher network. Meanwhile, we drive a category frequency smoothing technique via the teacher network to balance the generative training of each category. In the student distillation module, we explore a data inflation strategy to properly utilize a blend of real and synthetic data to train the student network via a classifier-sharing-based feature alignment technique. Intensive experiments across multiple benchmarks demonstrate that our HiDFD can achieve state-of-the-art performance using 120 times less collected data than existing methods. Code is available at this https URL.
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[CV-77] Novel AI Camera Camouflage: Face Cloaking Without Full Disguise

【速读】：该论文试图解决通过低可见度的面部伪装来规避现代面部识别系统的问题。解决方案的关键在于结合目标化的化妆品扰动和alpha透明层操作，对关键区域（如眉部、鼻梁和下颌线）进行细微修改，以显著干扰检测算法，而不依赖于明显的伪装。通过在密集面部关键点附近引入垂直扰动，并利用PNG图像中的alpha透明层攻击，实现双层效果：面部对人类观察者可见，但在机器可读的RGB层中消失，从而在反向图像搜索中无法识别。这种方法在保持隐蔽性的同时，有效对抗监控系统，实现可扩展的面部混淆策略。

链接: https://arxiv.org/abs/2412.13507
作者: David Noever,Forrest McKee
机构: 未知
关键词: combines targeted cosmetic, targeted cosmetic perturbations, evade modern facial, modern facial recognition, Microsoft Bing Visual
类目: Computer Vision and Pattern Recognition (cs.CV)
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点击查看摘要

Abstract:This study demonstrates a novel approach to facial camouflage that combines targeted cosmetic perturbations and alpha transparency layer manipulation to evade modern facial recognition systems. Unlike previous methods – such as CV dazzle, adversarial patches, and theatrical disguises – this work achieves effective obfuscation through subtle modifications to key-point regions, particularly the brow, nose bridge, and jawline. Empirical testing with Haar cascade classifiers and commercial systems like BetaFaceAPI and Microsoft Bing Visual Search reveals that vertical perturbations near dense facial key points significantly disrupt detection without relying on overt disguises. Additionally, leveraging alpha transparency attacks in PNG images creates a dual-layer effect: faces remain visible to human observers but disappear in machine-readable RGB layers, rendering them unidentifiable during reverse image searches. The results highlight the potential for creating scalable, low-visibility facial obfuscation strategies that balance effectiveness and subtlety, opening pathways for defeating surveillance while maintaining plausible anonymity.
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[CV-78] Urban Air Temperature Prediction using Conditional Diffusion Models

【速读】：该论文试图解决城市化进程中由于土地利用和土地覆盖（LULC）变化导致的2米高空气温（T_a）的高分辨率（HR）预测问题。由于传统气象站点的稀疏分布（如间隔超过10公里）以及数值模型的计算成本高昂，现有方法难以满足邻里尺度的高分辨率T_a数据需求。论文提出了一种新方法，利用土地表面温度（LST）和LULC相关特征，通过卫星影像获取数据，并首次采用扩散模型生成准确且视觉上逼真的高分辨率T_a地图。该方法的关键在于利用计算机视觉技术，通过扩散模型实现高分辨率T_a的预测，为气象研究提供了新的数据集基准，并为城市规划中的T_a影响模拟提供了工具。

链接: https://arxiv.org/abs/2412.13504
作者: Siyang Dai,Jun Liu,Ngai-Man Cheung
机构: Singapore University of Technology and Design(新加坡科技设计大学)
关键词: urban heat island, environmental challenges, heat island, global trend, trend has led
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
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Abstract:Urbanization as a global trend has led to many environmental challenges, including the urban heat island (UHI) effect. The increase in temperature has a significant impact on the well-being of urban residents. Air temperature ( T_a ) at 2m above the surface is a key indicator of the UHI effect. How land use land cover (LULC) affects T_a is a critical research question which requires high-resolution (HR) T_a data at neighborhood scale. However, weather stations providing T_a measurements are sparsely distributed e.g. more than 10km apart; and numerical models are impractically slow and computationally expensive. In this work, we propose a novel method to predict HR T_a at 100m ground separation distance (gsd) using land surface temperature (LST) and other LULC related features which can be easily obtained from satellite imagery. Our method leverages diffusion models for the first time to generate accurate and visually realistic HR T_a maps, which outperforms prior methods. We pave the way for meteorological research using computer vision techniques by providing a dataset of an extended spatial and temporal coverage, and a high spatial resolution as a benchmark for future research. Furthermore, we show that our model can be applied to urban planning by simulating the impact of different urban designs on T_a .
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[CV-79] Level-Set Parameters: Novel Representation for 3D Shape Analysis

【速读】：该论文试图解决传统三维形状分析中点云和网格数据离散性导致的输入分辨率变化问题。解决方案的关键在于引入神经场（neural fields）中的水平集参数（level-set parameters），通过有符号距离函数（signed distance functions）定义形状表面，从而提供一种连续且数值化的三维形状表示。与传统的欧几里得空间中的点云不同，水平集参数不具备欧几里得性质，因此论文通过将其建模为伪正态分布（pseudo-normal distribution）来建立不同形状之间的关联，并从数据集中学习分布先验。此外，论文提出通过超网络（hypernetwork）生成条件参数，以处理旋转和平移等形状变换，从而简化姿态相关的形状分析。该方法在形状分类、检索和6D物体姿态估计等应用中展示了其潜力。

链接: https://arxiv.org/abs/2412.13502
作者: Huan Lei,Hongdong Li,Andreas Geiger,Anthony Dick
机构: AIML, The University of Adelaide(阿德莱德大学); The Australian National University(澳大利亚国立大学); University of Tübingen(蒂宾根大学)
关键词: input resolutions, largely focused, discrete nature, susceptible to variations, variations in input
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:3D shape analysis has been largely focused on traditional 3D representations of point clouds and meshes, but the discrete nature of these data makes the analysis susceptible to variations in input resolutions. Recent development of neural fields brings in level-set parameters from signed distance functions as a novel, continuous, and numerical representation of 3D shapes, where the shape surfaces are defined as zero-level-sets of those functions. This motivates us to extend shape analysis from the traditional 3D data to these novel parameter data. Since the level-set parameters are not Euclidean like point clouds, we establish correlations across different shapes by formulating them as a pseudo-normal distribution, and learn the distribution prior from the respective dataset. To further explore the level-set parameters with shape transformations, we propose to condition a subset of these parameters on rotations and translations, and generate them with a hypernetwork. This simplifies the pose-related shape analysis compared to using traditional data. We demonstrate the promise of the novel representations through applications in shape classification (arbitrary poses), retrieval, and 6D object pose estimation. Code and data in this research are provided at this https URL.
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[CV-80] QueryCDR: Query-based Controllable Distortion Rectification Network for Fisheye Images ECCV2024

【速读】：该论文试图解决鱼眼图像校正中模型对不同畸变程度泛化能力不足的问题。解决方案的关键在于提出了一个新颖的基于查询的可控畸变校正网络（QueryCDR），其中核心创新包括畸变感知可学习查询机制（DLQM）和两种可控调制块。DLQM通过定义一系列可学习的查询来捕捉不同畸变程度的空间关系，从而实现位置依赖的校正控制条件，而可控调制块则进一步增强了这些控制条件对畸变特征的调制能力。这些组件协同工作，显著提升了模型在不同畸变程度下的泛化能力，实现了高质量且可控的畸变校正。

链接: https://arxiv.org/abs/2412.13496
作者: Pengbo Guo,Chengxu Liu,Xingsong Hou,Xueming Qian
机构: 未知
关键词: distortion, aims to correct, image rectification aims, Controllable Distortion Rectification, varying degrees
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Fisheye image rectification aims to correct distortions in images taken with fisheye cameras. Although current models show promising results on images with a similar degree of distortion as the training data, they will produce sub-optimal results when the degree of distortion changes and without retraining. The lack of generalization ability for dealing with varying degrees of distortion limits their practical application. In this paper, we take one step further to enable effective distortion rectification for images with varying degrees of distortion without retraining. We propose a novel Query-based Controllable Distortion Rectification network for fisheye images (QueryCDR). In particular, we first present the Distortion-aware Learnable Query Mechanism (DLQM), which defines the latent spatial relationships for different distortion degrees as a series of learnable queries. Each query can be learned to obtain position-dependent rectification control conditions, providing control over the rectification process. Then, we propose two kinds of controllable modulating blocks to enable the control conditions to guide the modulation of the distortion features better. These core components cooperate with each other to effectively boost the generalization ability of the model at varying degrees of distortion. Extensive experiments on fisheye image datasets with different distortion degrees demonstrate our approach achieves high-quality and controllable distortion rectification.
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[CV-81] Comparative Analysis of YOLOv9 YOLOv10 and RT-DETR for Real-Time Weed Detection

【速读】：该论文旨在解决智能喷洒应用中的杂草检测问题，特别是针对甜菜、单子叶植物和双子叶植物三类目标的检测。解决方案的关键在于评估和比较当前最先进的物体检测模型（如YOLOv9、YOLOv10和RT-DETR）在不同图像分辨率和模型变体（如nano、small、medium、large）下的平均精度（mAP）和推理时间。通过分析这些模型在不同GPU设备上的性能，研究揭示了推理时间和检测精度之间的权衡，为选择最适合实时杂草检测的模型提供了重要指导，从而推动高效智能喷洒系统的发展，提升农业生产力。

链接: https://arxiv.org/abs/2412.13490
作者: Ahmet Oğuz Saltık,Alicia Allmendinger,Anthony Stein
机构: University of Hohenheim (霍恩海姆大学); University of Hohenheim (霍恩海姆大学); University of Hohenheim (霍恩海姆大学)
关键词: smart-spraying applications focusing, object detection models, paper presents, presents a comprehensive, comprehensive evaluation
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:This paper presents a comprehensive evaluation of state-of-the-art object detection models, including YOLOv9, YOLOv10, and RT-DETR, for the task of weed detection in smart-spraying applications focusing on three classes: Sugarbeet, Monocot, and Dicot. The performance of these models is compared based on mean Average Precision (mAP) scores and inference times on different GPU devices. We consider various model variations, such as nano, small, medium, large alongside different image resolutions (320px, 480px, 640px, 800px, 960px). The results highlight the trade-offs between inference time and detection accuracy, providing valuable insights for selecting the most suitable model for real-time weed detection. This study aims to guide the development of efficient and effective smart spraying systems, enhancing agricultural productivity through precise weed management.
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[CV-82] Real-time One-Step Diffusion-based Expressive Portrait Videos Generation

【速读】：该论文试图解决生成式 AI 模型在生成高质量人像视频时速度过慢的问题，尤其是现有潜扩散模型（Latent Diffusion Models）在生成视频时需要多次采样步骤，导致生成一秒钟视频需要数分钟，严重限制了其实际应用。解决方案的关键在于提出了OSA-LCM（One-Step Avatar Latent Consistency Model），通过仅使用一次采样步骤实现实时生成，速度提升超过10倍。其核心创新包括：1）设计了一种新的avatar判别器（avatar discriminator），用于引导唇音同步和动作表现力，以在有限的采样步骤中提升视频质量；2）采用编辑微调方法（EFT）的第二阶段训练架构，将视频生成转化为编辑任务，有效解决了单步生成中的时间差距问题。实验结果表明，OSA-LCM在视频质量和效率上均优于现有的开源人像视频生成模型。

链接: https://arxiv.org/abs/2412.13479
作者: Hanzhong Guo,Hongwei Yi,Daquan Zhou,Alexander William Bergman,Michael Lingelbach,Yizhou Yu
机构: Hedra Inc; HKU; NUS
关键词: made great strides, Latent diffusion models, generating expressive portrait, single reference image, audio input
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Latent diffusion models have made great strides in generating expressive portrait videos with accurate lip-sync and natural motion from a single reference image and audio input. However, these models are far from real-time, often requiring many sampling steps that take minutes to generate even one second of video-significantly limiting practical use. We introduce OSA-LCM (One-Step Avatar Latent Consistency Model), paving the way for real-time diffusion-based avatars. Our method achieves comparable video quality to existing methods but requires only one sampling step, making it more than 10x faster. To accomplish this, we propose a novel avatar discriminator design that guides lip-audio consistency and motion expressiveness to enhance video quality in limited sampling steps. Additionally, we employ a second-stage training architecture using an editing fine-tuned method (EFT), transforming video generation into an editing task during training to effectively address the temporal gap challenge in single-step generation. Experiments demonstrate that OSA-LCM outperforms existing open-source portrait video generation models while operating more efficiently with a single sampling step.
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[CV-83] Enabling Region-Specific Control via Lassos in Point-Based Colorization AAAI2025

【速读】：该论文试图解决基于点的交互式图像着色技术中存在的颜色崩溃问题（color collapse），即当用户在语义相似区域提供不同颜色提示时，导致颜色混合和结果不理想的现象。其关键解决方案是引入套索工具（lasso tool）来控制每个颜色提示的范围，并通过设计框架利用用户提供的套索来定位注意力掩码（attention masks）。实验结果表明，使用单个套索的效果相当于应用4.18个单独的颜色提示，并且比仅使用点提示节省30%的时间。

链接: https://arxiv.org/abs/2412.13469
作者: Sanghyeon Lee,Jooyeol Yun,Jaegul Choo
机构: 未知
关键词: Point-based interactive colorization, interactive colorization techniques, effortlessly colorize grayscale, colorize grayscale images, Point-based interactive
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
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Abstract:Point-based interactive colorization techniques allow users to effortlessly colorize grayscale images using user-provided color hints. However, point-based methods often face challenges when different colors are given to semantically similar areas, leading to color intermingling and unsatisfactory results-an issue we refer to as color collapse. The fundamental cause of color collapse is the inadequacy of points for defining the boundaries for each color. To mitigate color collapse, we introduce a lasso tool that can control the scope of each color hint. Additionally, we design a framework that leverages the user-provided lassos to localize the attention masks. The experimental results show that using a single lasso is as effective as applying 4.18 individual color hints and can achieve the desired outcomes in 30% less time than using points alone.
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[CV-84] FlexPose: Pose Distribution Adaptation with Limited Guidance AAAI25

【速读】：该论文试图解决在标注新收集图像的人体姿态时，标注过程成本高且耗时的问题。解决方案的关键在于利用不同数据集中人体姿态分布的相似性，即它们共享相似的姿态结构先验（pose hinge-structure priors），但具有不同的几何变换（如旋转、关节角度和骨骼长度比例）。论文提出了一种名为FlexPose的方法，通过微调预训练姿态生成器中的少量线性层，使其适应新的姿态分布，从而在仅提供少量标注指导的情况下生成与目标姿态相似的标注。这种方法的核心在于将人体姿态关节坐标表示为骨骼图像，并通过有限的标注指导实现生成器的迁移学习，从而在跨数据集设置中实现最先进的性能。

链接: https://arxiv.org/abs/2412.13463
作者: Zixiao Wang,Junwu Weng,Mengyuan Liu,Bei Yu
机构: 1. Tsinghua University (清华大学);
2. Beijing Institute of Technology (北京理工大学);
3. Institute of Automation, Chinese Academy of Sciences (中国科学院自动化研究所)
关键词: Numerous well-annotated human, Numerous well-annotated, well-annotated human key-point, human key-point datasets, Pose
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted by AAAI25, 12 pages, 10 figures

点击查看摘要

Abstract:Numerous well-annotated human key-point datasets are publicly available to date. However, annotating human poses for newly collected images is still a costly and time-consuming progress. Pose distributions from different datasets share similar pose hinge-structure priors with different geometric transformations, such as pivot orientation, joint rotation, and bone length ratio. The difference between Pose distributions is essentially the difference between the transformation distributions. Inspired by this fact, we propose a method to calibrate a pre-trained pose generator in which the pose prior has already been learned to an adapted one following a new pose distribution. We treat the representation of human pose joint coordinates as skeleton image and transfer a pre-trained pose annotation generator with only a few annotation guidance. By fine-tuning a limited number of linear layers that closely related to the pose transformation, the adapted generator is able to produce any number of pose annotations that are similar to the target poses. We evaluate our proposed method, FlexPose, on several cross-dataset settings both qualitatively and quantitatively, which demonstrates that our approach achieves state-of-the-art performance compared to the existing generative-model-based transfer learning methods when given limited annotation guidance.
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[CV-85] Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection AAAI2025

【速读】：该论文试图解决三维异常检测中现有方法主要关注外部结构而忽略内部信息的问题。解决方案的关键在于引入了一种名为内部空间模态感知 (Internal Spatial Modality Perception, ISMP) 的方法，通过空间洞察引擎 (Spatial Insight Engine, SIE) 从内部视角抽象出点云的复杂内部信息，并将其转化为全局特征。此外，论文还提出了增强的关键点特征提取模块和特征过滤模块，分别用于增强空间结构特征表示和减少噪声与冗余特征，从而更精确地对齐空间结构。实验结果表明，该方法在Real3D-AD基准测试中显著提升了物体级和像素级的AUROC，分别为4.2%和13.1%。

链接: https://arxiv.org/abs/2412.13461
作者: Hanzhe Liang,Guoyang Xie,Chengbin Hou,Bingshu Wang,Can Gao,Jinbao Wang
机构: 未知
关键词: computer vision, anomaly detection, anomaly detection performance, significant focus, focus in computer
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
备注: AAAI2025 Accepted

点击查看摘要

Abstract:3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks.
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[CV-86] Pre-training a Density-Aware Pose Transformer for Robust LiDAR-based 3D Human Pose Estimation AAAI2025

【速读】：该论文试图解决基于LiDAR的3D人体姿态估计（3D HPE）中由于点云噪声和稀疏性导致的鲁棒性不足问题。解决方案的关键在于通过建模低质量点云的内在属性来获取足够的3D HPE信息，而不依赖于时间信息、多模态融合或SMPL优化。具体来说，论文提出了一个简洁而有效的密度感知姿态变换器（DAPT），通过联合锚点和精心设计的交换模块从不同密度的点云中提取有效信息，并使用1D热图表示关键点的精确位置。此外，论文还提出了一种全面的LiDAR人体合成与增强方法，通过预训练模型来增强人体姿态的先验知识，并通过随机采样人体位置和方向以及模拟遮挡来增加点云的多样性。实验结果表明，该方法在多个数据集上均达到了最先进的性能。

链接: https://arxiv.org/abs/2412.13454
作者: Xiaoqi An,Lin Zhao,Chen Gong,Jun Li,Jian Yang
机构: 未知
关键词: Human Pose Estimation, Pose Estimation, pose estimation remains, point clouds, autonomous driving
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to AAAI 2025

点击查看摘要

Abstract:With the rapid development of autonomous driving, LiDAR-based 3D Human Pose Estimation (3D HPE) is becoming a research focus. However, due to the noise and sparsity of LiDAR-captured point clouds, robust human pose estimation remains challenging. Most of the existing methods use temporal information, multi-modal fusion, or SMPL optimization to correct biased results. In this work, we try to obtain sufficient information for 3D HPE only by modeling the intrinsic properties of low-quality point clouds. Hence, a simple yet powerful method is proposed, which provides insights both on modeling and augmentation of point clouds. Specifically, we first propose a concise and effective density-aware pose transformer (DAPT) to get stable keypoint representations. By using a set of joint anchors and a carefully designed exchange module, valid information is extracted from point clouds with different densities. Then 1D heatmaps are utilized to represent the precise locations of the keypoints. Secondly, a comprehensive LiDAR human synthesis and augmentation method is proposed to pre-train the model, enabling it to acquire a better human body prior. We increase the diversity of point clouds by randomly sampling human positions and orientations and by simulating occlusions through the addition of laser-level masks. Extensive experiments have been conducted on multiple datasets, including IMU-annotated LidarHuman26M, SLOPER4D, and manually annotated Waymo Open Dataset v2.0 (Waymo), HumanM3. Our method demonstrates SOTA performance in all scenarios. In particular, compared with LPFormer on Waymo, we reduce the average MPJPE by 10.0mm . Compared with PRN on SLOPER4D, we notably reduce the average MPJPE by 20.7mm .
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[CV-87] ConDo: Continual Domain Expansion for Absolute Pose Regression AAAI2025

【速读】：该论文试图解决在持续变化的环境中，基于固定数据集训练的绝对位姿回归 (Absolute Pose Regression, APR) 模型容易过拟合，导致在新场景或新条件下表现不佳的问题。解决方案的关键是提出了持续域扩展 (Continual Domain Expansion, ConDo) 方法，通过不断收集未标记的推理数据并利用场景无关的定位方法进行知识蒸馏，从而有效扩展APR的泛化域。ConDo通过均匀采样历史和新增数据，显著提升了模型在长期数据变化下的鲁棒性，并在大规模基准测试中表现出优于基线的性能。

链接: https://arxiv.org/abs/2412.13452
作者: Zijun Li,Zhipeng Cai,Bochun Yang,Xuelun Shen,Siqi Shen,Xiaoliang Fan,Michael Paulitsch,Cheng Wang
机构: Zijun Li1\equalcontrib; Bochun Yang1; Xuelun Shen1; Siqi Shen1; Xiaoliang Fan1; Cheng Wang122footnotemark: 2; Zhipeng Cai2\equalcontrib; Michael Paulitsch2
关键词: machine learning problem, fundamental machine learning, Visual localization, Absolute Pose Regression, learning problem
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: AAAI2025

点击查看摘要

Abstract:Visual localization is a fundamental machine learning problem. Absolute Pose Regression (APR) trains a scene-dependent model to efficiently map an input image to the camera pose in a pre-defined scene. However, many applications have continually changing environments, where inference data at novel poses or scene conditions (weather, geometry) appear after deployment. Training APR on a fixed dataset leads to overfitting, making it fail catastrophically on challenging novel data. This work proposes Continual Domain Expansion (ConDo), which continually collects unlabeled inference data to update the deployed APR. Instead of applying standard unsupervised domain adaptation methods which are ineffective for APR, ConDo effectively learns from unlabeled data by distilling knowledge from scene-agnostic localization methods. By sampling data uniformly from historical and newly collected data, ConDo can effectively expand the generalization domain of APR. Large-scale benchmarks with various scene types are constructed to evaluate models under practical (long-term) data changes. ConDo consistently and significantly outperforms baselines across architectures, scene types, and data changes. On challenging scenes (Fig.1), it reduces the localization error by 7x (14.8m vs 1.7m). Analysis shows the robustness of ConDo against compute budgets, replay buffer sizes and teacher prediction noise. Comparing to model re-training, ConDo achieves similar performance up to 25x faster.
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[CV-88] DarkIR: Robust Low-Light Image Restoration

【速读】：该论文试图解决夜间或暗光条件下摄影中常见的噪声、低光和模糊问题，特别是在长曝光条件下。解决方案的关键在于提出了一种高效且鲁棒的多任务低光图像恢复神经网络（DarkIR），该网络通过引入新的注意力机制来增强高效卷积神经网络（CNN）的感受野，从而在减少参数和MAC操作的计算成本的同时，实现了对去模糊（Deblurring）和低光图像增强（LLIE）任务的联合处理。与基于Transformer的模型不同，DarkIR在LOLBlur、LOLv2和Real-LOLBlur等数据集上取得了最新的最先进结果，并展示了在真实世界夜间和暗光图像上的良好泛化能力。

链接: https://arxiv.org/abs/2412.13443
作者: Daniel Feijoo,Juan C. Benito,Alvaro Garcia,Marcos V. Conde
机构: Cidaut AI, Spain; Computer Vision Lab, University of Würzburg
关键词: blurring issues due, conditions typically suffers, Low-light Image Enhancement, dark conditions typically, suffers from noise
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注: Technical Report

点击查看摘要

Abstract:Photography during night or in dark conditions typically suffers from noise, low light and blurring issues due to the dim environment and the common use of long exposure. Although Deblurring and Low-light Image Enhancement (LLIE) are related under these conditions, most approaches in image restoration solve these tasks separately. In this paper, we present an efficient and robust neural network for multi-task low-light image restoration. Instead of following the current tendency of Transformer-based models, we propose new attention mechanisms to enhance the receptive field of efficient CNNs. Our method reduces the computational costs in terms of parameters and MAC operations compared to previous methods. Our model, DarkIR, achieves new state-of-the-art results on the popular LOLBlur, LOLv2 and Real-LOLBlur datasets, being able to generalize on real-world night and dark images. Code and models at this https URL
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[CV-89] Exploring Transformer-Augmented LSTM for Temporal and Spatial Feature Learning in Trajectory Prediction

【速读】：该论文试图解决车辆轨迹预测的准确性问题，以确保自动驾驶的安全性和效率。解决方案的关键在于结合基于Transformer的模型与基于长短期记忆网络（LSTM）的技术，以增强空间和时间特征的学习。具体来说，论文提出了一种混合模型，该模型使用LSTM进行时间编码，并利用Transformer编码器捕捉车辆间的复杂交互。通过在基于网格的环境中处理邻近车辆的空间轨迹特征，并结合车辆的时间轨迹数据，模型通过LSTM编码和Transformer注意力层进行学习。尽管该模型在性能上未超越其前身LSTM方法，但它展示了将Transformer与LSTM技术结合以构建可解释轨迹预测模型的潜力。

链接: https://arxiv.org/abs/2412.13419
作者: Chandra Raskoti,Weizi Li
机构: University of Tennessee, Knoxville, TN, USA(田纳西大学诺克斯维尔分校, 田纳西州, 美国); Min H. Kao Department of Electrical Engineering and Computer Science(Min H. Kao电气工程与计算机科学系)
关键词: efficient autonomous driving, Accurate vehicle trajectory, Long Short-Term Memory, Accurate vehicle, trajectory prediction
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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Abstract:Accurate vehicle trajectory prediction is crucial for ensuring safe and efficient autonomous driving. This work explores the integration of Transformer based model with Long Short-Term Memory (LSTM) based technique to enhance spatial and temporal feature learning in vehicle trajectory prediction. Here, a hybrid model that combines LSTMs for temporal encoding with a Transformer encoder for capturing complex interactions between vehicles is proposed. Spatial trajectory features of the neighboring vehicles are processed and goes through a masked scatter mechanism in a grid based environment, which is then combined with temporal trajectory of the vehicles. This combined trajectory data are learned by sequential LSTM encoding and Transformer based attention layers. The proposed model is benchmarked against predecessor LSTM based methods, including STA-LSTM, SA-LSTM, CS-LSTM, and NaiveLSTM. Our results, while not outperforming it’s predecessor, demonstrate the potential of integrating Transformers with LSTM based technique to build interpretable trajectory prediction model. Future work will explore alternative architectures using Transformer applications to further enhance performance. This study provides a promising direction for improving trajectory prediction models by leveraging transformer based architectures, paving the way for more robust and interpretable vehicle trajectory prediction system.
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[CV-90] Zero-Shot Low Light Image Enhancement with Diffusion Prior

【速读】：该论文试图解决低光图像增强 (Low Light Image Enhancement, LLIE) 中，如何在保持图像美学质量的同时，避免生成式模型（如扩散模型）引入的幻觉问题。解决方案的关键在于提出了一种新颖的零样本方法，用于控制和优化扩散模型在暗到亮图像转换任务中的生成行为。该方法通过精确调控生成过程，避免了非现有元素的引入或原始场景视觉语义的显著改变，从而在低光图像增强任务中表现优于现有的最先进方法。

链接: https://arxiv.org/abs/2412.13401
作者: Joshua Cho,Sara Aghajanzadeh,Zhen Zhu,D. A. Forsyth
机构: University of Illinois Urbana-Champaign (伊利诺伊大学厄巴纳-香槟分校)
关键词: Balancing aesthetic quality, Balancing aesthetic, degraded sources, computational photography, aesthetic quality
类目: Computer Vision and Pattern Recognition (cs.CV)
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点击查看摘要

Abstract:Balancing aesthetic quality with fidelity when enhancing images from challenging, degraded sources is a core objective in computational photography. In this paper, we address low light image enhancement (LLIE), a task in which dark images often contain limited visible information. Diffusion models, known for their powerful image enhancement capacities, are a natural choice for this problem. However, their deep generative priors can also lead to hallucinations, introducing non-existent elements or substantially altering the visual semantics of the original scene. In this work, we introduce a novel zero-shot method for controlling and refining the generative behavior of diffusion models for dark-to-light image conversion tasks. Our method demonstrates superior performance over existing state-of-the-art methods in the task of low-light image enhancement, as evidenced by both quantitative metrics and qualitative analysis.
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[CV-91] Distribution Shifts at Scale: Out-of-distribution Detection in Earth Observation

【速读】：该论文试图解决在地球观测领域中，深度学习模型在面对分布偏移（distribution shifts）时性能下降的问题，尤其是在数据稀缺的地区。解决方案的关键在于提出了一种后处理方法TARDIS，用于大规模地理空间部署中的分布外（OOD）检测。TARDIS的核心创新在于通过整合已知分布（ID）数据和未知分布的信息生成代理标签（surrogate labels），从而实现大规模的OOD检测。具体来说，TARDIS利用预训练模型、ID数据和WILD样本，通过内部激活信息将WILD样本分解为代理ID和代理OOD标签，并拟合一个二分类器作为OOD检测器。该方法在多个实验设置中表现接近理论上限，展示了其在大规模部署中的有效性和可扩展性。

链接: https://arxiv.org/abs/2412.13394
作者: Burak Ekim,Girmaw Abebe Tadesse,Caleb Robinson,Gilles Hacheme,Michael Schmitt,Rahul Dodhia,Juan M. Lavista Ferres
机构: University of the Bundeswehr Munich; Microsoft AI for Good Research Lab
关键词: Training robust deep, Earth Observation, robust deep learning, Training robust, critical in Earth
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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点击查看摘要

Abstract:Training robust deep learning models is critical in Earth Observation, where globally deployed models often face distribution shifts that degrade performance, especially in low-data regions. Out-of-distribution (OOD) detection addresses this challenge by identifying inputs that differ from in-distribution (ID) data. However, existing methods either assume access to OOD data or compromise primary task performance, making them unsuitable for real-world deployment. We propose TARDIS, a post-hoc OOD detection method for scalable geospatial deployments. The core novelty lies in generating surrogate labels by integrating information from ID data and unknown distributions, enabling OOD detection at scale. Our method takes a pre-trained model, ID data, and WILD samples, disentangling the latter into surrogate ID and surrogate OOD labels based on internal activations, and fits a binary classifier as an OOD detector. We validate TARDIS on EuroSAT and xBD datasets, across 17 experimental setups covering covariate and semantic shifts, showing that it performs close to the theoretical upper bound in assigning surrogate ID and OOD samples in 13 cases. To demonstrate scalability, we deploy TARDIS on the Fields of the World dataset, offering actionable insights into pre-trained model behavior for large-scale deployments. The code is publicly available at this https URL.
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[CV-92] MMHMR: Generative Masked Modeling for Hand Mesh Recovery

【速读】：该论文试图解决从单张RGB图像重建3D手部网格的挑战，主要难点在于复杂的关节运动、自遮挡以及深度模糊性。传统判别方法（discriminative methods）在处理2D到3D映射中的固有模糊性时表现不佳。论文提出的解决方案是MMHMR，一种新颖的生成式掩码模型（generative masked model），通过学习并从2D到3D映射过程的概率分布中采样来合成合理的3D手部网格。MMHMR的关键在于两个组件：(1) VQ-MANO，它将3D手部关节运动编码为潜在空间中的离散姿态令牌（discrete pose tokens）；(2) 上下文引导的掩码Transformer（Context-Guided Masked Transformer），它随机掩码姿态令牌并学习其联合分布，条件是损坏的令牌序列、图像上下文和2D姿态线索。这种学习到的分布在推理过程中支持置信度引导的采样，从而生成低不确定性和高精度的网格重建。

链接: https://arxiv.org/abs/2412.13393
作者: Muhammad Usama Saleem,Ekkasit Pinyoanuntapong,Mayur Jagdishbhai Patel,Hongfei Xue,Ahmed Helmy,Srijan Das,Pu Wang
机构: University of North Carolina at Charlotte (北卡罗来纳大学夏洛特分校)
关键词: single RGB image, single RGB, RGB image, challenging due, due to complex
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:Reconstructing a 3D hand mesh from a single RGB image is challenging due to complex articulations, self-occlusions, and depth ambiguities. Traditional discriminative methods, which learn a deterministic mapping from a 2D image to a single 3D mesh, often struggle with the inherent ambiguities in 2D-to-3D mapping. To address this challenge, we propose MMHMR, a novel generative masked model for hand mesh recovery that synthesizes plausible 3D hand meshes by learning and sampling from the probabilistic distribution of the ambiguous 2D-to-3D mapping process. MMHMR consists of two key components: (1) a VQ-MANO, which encodes 3D hand articulations as discrete pose tokens in a latent space, and (2) a Context-Guided Masked Transformer that randomly masks out pose tokens and learns their joint distribution, conditioned on corrupted token sequences, image context, and 2D pose cues. This learned distribution facilitates confidence-guided sampling during inference, producing mesh reconstructions with low uncertainty and high precision. Extensive evaluations on benchmark and real-world datasets demonstrate that MMHMR achieves state-of-the-art accuracy, robustness, and realism in 3D hand mesh reconstruction. Project website: this https URL
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[CV-93] Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion

【速读】：该论文试图解决深度补全问题，即将稀疏的深度测量数据转化为密集的深度图，特别是在深度测量数据稀疏、不规则分布或密度变化的情况下。解决方案的关键在于将深度补全重新定义为图像条件下的深度图生成任务，并利用预训练的单目深度估计潜在扩散模型 (latent diffusion model)，通过优化方案在去噪扩散的迭代推理过程中注入稀疏深度观测数据。这种方法（Marigold-DC）展示了在多种环境中优异的零样本泛化能力，并能有效处理极其稀疏的指导数据。

链接: https://arxiv.org/abs/2412.13389
作者: Massimiliano Viola,Kevin Qu,Nando Metzger,Bingxin Ke,Alexander Becker,Konrad Schindler,Anton Obukhov
机构: ETH Zürich (苏黎世联邦理工学院)
关键词: Depth, upgrades sparse depth, Depth completion upgrades, completion upgrades sparse, sparse depth measurements
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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Abstract:Depth completion upgrades sparse depth measurements into dense depth maps guided by a conventional image. Existing methods for this highly ill-posed task operate in tightly constrained settings and tend to struggle when applied to images outside the training domain or when the available depth measurements are sparse, irregularly distributed, or of varying density. Inspired by recent advances in monocular depth estimation, we reframe depth completion as an image-conditional depth map generation guided by sparse measurements. Our method, Marigold-DC, builds on a pretrained latent diffusion model for monocular depth estimation and injects the depth observations as test-time guidance via an optimization scheme that runs in tandem with the iterative inference of denoising diffusion. The method exhibits excellent zero-shot generalization across a diverse range of environments and handles even extremely sparse guidance effectively. Our results suggest that contemporary monocular depth priors greatly robustify depth completion: it may be better to view the task as recovering dense depth from (dense) image pixels, guided by sparse depth; rather than as inpainting (sparse) depth, guided by an image. Project website: this https URL
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[CV-94] argeted View-Invariant Adversarial Perturbations for 3D Object Recognition AAAI-25

【速读】：该论文试图解决3D物体识别中的对抗攻击问题，特别是在多视角分析场景下，物体可以从不同角度观察时面临的挑战。解决方案的关键是提出了视图不变对抗扰动 (View-Invariant Adversarial Perturbations, VIAP)，这是一种能够在多个视角下保持有效性的新型对抗样本生成方法。与传统方法不同，VIAP不仅能够实现目标攻击，将识别系统引导至预定的标签，而且仅需使用单一的通用扰动。通过在包含1210张图像和121个不同3D物体的数据集上进行实验，VIAP在目标攻击和非目标攻击设置下均表现出显著效果，特别是在目标攻击中，其top-1准确率在不同epsilon值下均超过95%。这一方法为3D识别系统的鲁棒性测试提供了新的基准，推动了对抗机器学习在3D物体识别领域的发展。

链接: https://arxiv.org/abs/2412.13376
作者: Christian Green,Mehmet Ergezer,Abdurrahman Zeybey
机构: Wentworth Institute of Technology; Amazon
关键词: pose significant challenges, scenarios involving multi-view, involving multi-view analysis, attacks pose significant, varying angles
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Image and Video Processing (eess.IV)
备注: Accepted to AAAI-25 Workshop on Artificial Intelligence for Cyber Security (AICS): this http URL

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Abstract:Adversarial attacks pose significant challenges in 3D object recognition, especially in scenarios involving multi-view analysis where objects can be observed from varying angles. This paper introduces View-Invariant Adversarial Perturbations (VIAP), a novel method for crafting robust adversarial examples that remain effective across multiple viewpoints. Unlike traditional methods, VIAP enables targeted attacks capable of manipulating recognition systems to classify objects as specific, pre-determined labels, all while using a single universal perturbation. Leveraging a dataset of 1,210 images across 121 diverse rendered 3D objects, we demonstrate the effectiveness of VIAP in both targeted and untargeted settings. Our untargeted perturbations successfully generate a singular adversarial noise robust to 3D transformations, while targeted attacks achieve exceptional results, with top-1 accuracies exceeding 95% across various epsilon values. These findings highlight VIAPs potential for real-world applications, such as testing the robustness of 3D recognition systems. The proposed method sets a new benchmark for view-invariant adversarial robustness, advancing the field of adversarial machine learning for 3D object recognition.
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[CV-95] Bringing Multimodality to Amazon Visual Search System

【速读】：该论文试图解决图像到图像匹配中的误报问题，即由于匹配局部视觉模式导致的错误匹配。解决方案的关键在于引入视觉-语言预训练（vision-language pretraining）的最新进展，通过在深度度量学习中加入额外的图像-文本对齐损失（image-text alignment losses），作为对图像到图像匹配损失的约束。这种额外的对齐损失使得模型能够从图像和文本两种模态中显式学习概念，从而避免匹配低级视觉特征。论文进一步提出了两种变体模型：3-tower模型和4-tower模型，后者额外引入短文本查询输入，显著提升了图像匹配的点击率（CTR），分别实现了4.95%和1.13%的相对提升。

链接: https://arxiv.org/abs/2412.13364
作者: Xinliang Zhu,Michael Huang,Han Ding,Jinyu Yang,Kelvin Chen,Tao Zhou,Tal Neiman,Ouye Xie,Son Tran,Benjamin Yao,Doug Gray,Anuj Bindal,Arnab Dhua
机构: Amazon.com(亚马逊); Amazon.com(亚马逊); Amazon.com(亚马逊); Amazon.com(亚马逊); Amazon.com(亚马逊); Amazon.com(亚马逊); Amazon.com(亚马逊); Amazon.com(亚马逊); Amazon.com(亚马逊); Amazon.com(亚马逊); Amazon.com(亚马逊); Amazon.com(亚马逊); Amazon.com(亚马逊); Amazon.com(亚马逊)
关键词: computer vision community, vision community, computer vision, Image, matching
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Image to image matching has been well studied in the computer vision community. Previous studies mainly focus on training a deep metric learning model matching visual patterns between the query image and gallery images. In this study, we show that pure image-to-image matching suffers from false positives caused by matching to local visual patterns. To alleviate this issue, we propose to leverage recent advances in vision-language pretraining research. Specifically, we introduce additional image-text alignment losses into deep metric learning, which serve as constraints to the image-to-image matching loss. With additional alignments between the text (e.g., product title) and image pairs, the model can learn concepts from both modalities explicitly, which avoids matching low-level visual features. We progressively develop two variants, a 3-tower and a 4-tower model, where the latter takes one more short text query input. Through extensive experiments, we show that this change leads to a substantial improvement to the image to image matching problem. We further leveraged this model for multimodal search, which takes both image and reformulation text queries to improve search quality. Both offline and online experiments show strong improvements on the main metrics. Specifically, we see 4.95% relative improvement on image matching click through rate with the 3-tower model and 1.13% further improvement from the 4-tower model.
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[CV-96] BadSAD: Clean-Label Backdoor Attacks against Deep Semi-Supervised Anomaly Detection

【速读】：该论文试图解决深度学习模型在图像异常检测 (Image Anomaly Detection, IAD) 应用中易受后门攻击 (backdoor attacks) 的问题。解决方案的关键在于提出了一种名为 BadSAD 的新型后门攻击框架，专门针对 Deep Semi-Supervised Anomaly Detection (DeepSAD) 模型。该框架包含两个核心阶段：触发器注入 (trigger injection)，即在正常图像中嵌入微妙的触发器；以及潜在空间操纵 (latent space manipulation)，通过将中毒图像定位并聚类在正常图像附近，使触发器显得无害。实验结果表明，该攻击策略在基准数据集上具有显著效果，突显了后门攻击对基于深度学习的异常检测系统的严重威胁。

链接: https://arxiv.org/abs/2412.13324
作者: He Cheng,Depeng Xu,Shuhan Yuan
机构: Utah State University(犹他州立大学); University of North Carolina at Charlotte(北卡罗来纳大学夏洛特分校)
关键词: anomaly detection, medical imaging, industrial inspection, Image anomaly detection, Semi-Supervised Anomaly Detection
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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Abstract:Image anomaly detection (IAD) is essential in applications such as industrial inspection, medical imaging, and security. Despite the progress achieved with deep learning models like Deep Semi-Supervised Anomaly Detection (DeepSAD), these models remain susceptible to backdoor attacks, presenting significant security challenges. In this paper, we introduce BadSAD, a novel backdoor attack framework specifically designed to target DeepSAD models. Our approach involves two key phases: trigger injection, where subtle triggers are embedded into normal images, and latent space manipulation, which positions and clusters the poisoned images near normal images to make the triggers appear benign. Extensive experiments on benchmark datasets validate the effectiveness of our attack strategy, highlighting the severe risks that backdoor attacks pose to deep learning-based anomaly detection systems.
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[CV-97] FastVLM: Efficient Vision Encoding for Vision Language Models

【速读】：该论文试图解决在高分辨率图像输入下，视觉语言模型（Vision Language Models, VLMs）中视觉编码器（如ViTs）因大量tokens和高编码延迟导致的效率问题。解决方案的关键在于FastVLM模型，它通过引入FastViTHD这一新型混合视觉编码器，能够在高分辨率图像下输出更少的tokens并显著减少编码时间。与以往方法不同，FastVLM通过仅调整输入图像分辨率来实现视觉token数量与图像分辨率之间的最佳平衡，无需额外的token剪枝，简化了模型设计。在LLaVA-1.5设置下，FastVLM在保持与先前工作相似性能的同时，将首次token生成时间（TTFT）提升了3.2倍，并且在高分辨率（1152×1152）下，相比LLaVa-OneVision，FastVLM在关键基准测试中表现相当，但TTFT快了85倍，视觉编码器体积小了3.4倍。

链接: https://arxiv.org/abs/2412.13303
作者: Pavan Kumar Anasosalu Vasu,Fartash Faghri,Chun-Liang Li,Cem Koc,Nate True,Albert Antony,Gokul Santhanam,James Gabriel,Peter Grasch,Oncel Tuzel,Hadi Pouransari
机构: Apple(苹果)
关键词: Vision Language Models, image understanding tasks, text-rich image understanding, Vision Language, Language Models
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:Scaling the input image resolution is essential for enhancing the performance of Vision Language Models (VLMs), particularly in text-rich image understanding tasks. However, popular visual encoders such as ViTs become inefficient at high resolutions due to the large number of tokens and high encoding latency caused by stacked self-attention layers. At different operational resolutions, the vision encoder of a VLM can be optimized along two axes: reducing encoding latency and minimizing the number of visual tokens passed to the LLM, thereby lowering overall latency. Based on a comprehensive efficiency analysis of the interplay between image resolution, vision latency, token count, and LLM size, we introduce FastVLM, a model that achieves an optimized trade-off between latency, model size and accuracy. FastVLM incorporates FastViTHD, a novel hybrid vision encoder designed to output fewer tokens and significantly reduce encoding time for high-resolution images. Unlike previous methods, FastVLM achieves the optimal balance between visual token count and image resolution solely by scaling the input image, eliminating the need for additional token pruning and simplifying the model design. In the LLaVA-1.5 setup, FastVLM achieves 3.2 \times improvement in time-to-first-token (TTFT) while maintaining similar performance on VLM benchmarks compared to prior works. Compared to LLaVa-OneVision at the highest resolution (1152 \times 1152), FastVLM achieves comparable performance on key benchmarks like SeedBench and MMMU, using the same 0.5B LLM, but with 85 \times faster TTFT and a vision encoder that is 3.4 \times smaller.
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[CV-98] Image registration is a geometric deep learning task

【速读】：该论文试图解决数据驱动型可变形图像配准方法中由于网格重采样操作导致的误差问题，特别是在处理稀疏、高维特征网格时的影响。解决方案的关键在于引入了一种基于几何深度学习（geometric deep-learning）原理的新范式，通过将图像特征建模为在欧几里得空间中自由移动的节点，利用图操作更新节点坐标并动态调整局部邻域，从而避免了传统方法中在每个变形步骤之间进行网格重采样的需求。这种方法构建了一个多分辨率的可变形配准模型，能够在不进行中间重采样操作的情况下，迭代地优化整体变换，显著减少了由于重采样引入的误差，并在多个医学图像配准任务中展示了与当前最先进方法相当的表现。

链接: https://arxiv.org/abs/2412.13294
作者: Vasiliki Sideri-Lampretsa,Nil Stolt-Ansó,Martin Menten,Huaqi Qiu,Julian McGinnis,Daniel Rueckert
机构: Technical University Munich(慕尼黑工业大学)
关键词: process grid-like inputs, methods predominantly rely, grid-like inputs, predominantly rely, Data-driven deformable image
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 22 Pages

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Abstract:Data-driven deformable image registration methods predominantly rely on operations that process grid-like inputs. However, applying deformable transformations to an image results in a warped space that deviates from a rigid grid structure. Consequently, data-driven approaches with sequential deformations have to apply grid resampling operations between each deformation step. While artifacts caused by resampling are negligible in high-resolution images, the resampling of sparse, high-dimensional feature grids introduces errors that affect the deformation modeling process. Taking inspiration from Lagrangian reference frames of deformation fields, our work introduces a novel paradigm for data-driven deformable image registration that utilizes geometric deep-learning principles to model deformations without grid requirements. Specifically, we model image features as a set of nodes that freely move in Euclidean space, update their coordinates under graph operations, and dynamically readjust their local neighborhoods. We employ this formulation to construct a multi-resolution deformable registration model, where deformation layers iteratively refine the overall transformation at each resolution without intermediate resampling operations on the feature grids. We investigate our method’s ability to fully deformably capture large deformations across a number of medical imaging registration tasks. In particular, we apply our approach (GeoReg) to the registration of inter-subject brain MR images and inhale-exhale lung CT images, showing on par performance with the current state-of-the-art methods. We believe our contribution open up avenues of research to reduce the black-box nature of current learned registration paradigms by explicitly modeling the transformation within the architecture.
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[CV-99] CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices

【速读】：该论文试图解决现有光流预测模型在移动设备上速度和内存使用受限的问题。解决方案的关键在于提出了一种名为CompactFlowNet的实时移动神经网络架构，该架构专为移动设备优化，通过改进训练流程来减少模型权重、降低内存占用并提高速度，同时保持较低的误差。该方法在KITTI和Sintel基准测试中表现出与现有轻量级模型相当或更优的性能，并在iPhone 8等设备上实现了实时操作效率。

链接: https://arxiv.org/abs/2412.13273
作者: Andrei Znobishchev,Valerii Filev,Oleg Kudashev,Nikita Orlov,Humphrey Shi
机构: Picsart AI Research (PAIR); SHILabs @ U of Oregon &&& UIUC
关键词: initial frame relative, optical flow prediction, mobile neural network, optical flow, initial frame
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:We present CompactFlowNet, the first real-time mobile neural network for optical flow prediction, which involves determining the displacement of each pixel in an initial frame relative to the corresponding pixel in a subsequent frame. Optical flow serves as a fundamental building block for various video-related tasks, such as video restoration, motion estimation, video stabilization, object tracking, action recognition, and video generation. While current state-of-the-art methods prioritize accuracy, they often overlook constraints regarding speed and memory usage. Existing light models typically focus on reducing size but still exhibit high latency, compromise significantly on quality, or are optimized for high-performance GPUs, resulting in sub-optimal performance on mobile devices. This study aims to develop a mobile-optimized optical flow model by proposing a novel mobile device-compatible architecture, as well as enhancements to the training pipeline, which optimize the model for reduced weight, low memory utilization, and increased speed while maintaining minimal error. Our approach demonstrates superior or comparable performance to the state-of-the-art lightweight models on the challenging KITTI and Sintel benchmarks. Furthermore, it attains a significantly accelerated inference speed, thereby yielding real-time operational efficiency on the iPhone 8, while surpassing real-time performance levels on more advanced mobile devices.
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[CV-100] RBSM: A Deep Implicit 3D Breast Shape Model

【速读】：该论文旨在解决女性乳房三维形状建模的问题，特别是改进了基于主成分分析 (PCA) 的Regensburg Breast Shape Model (RBSM)。其关键解决方案是采用隐式神经表示 (implicit neural representations) 来替代传统的PCA方法，从而能够直接从原始的三维乳房扫描数据中进行训练，避免了计算密集型的非刚性配准 (non-rigid registration) 过程，这一过程在处理无特征的乳房形状时尤为困难。新模型iRBSM不仅能够捕捉包括乳头和肚脐在内的精细表面结构，而且在表面重建任务中表现优于RBSM，并展示了从单张图像进行三维乳房形状重建的应用潜力。

链接: https://arxiv.org/abs/2412.13244
作者: Maximilian Weiherer,Antonia von Riedheim,Vanessa Brébant,Bernhard Egger,Christoph Palm
机构: Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)(埃尔朗根-纽伦堡大学); Université Paris-Saclay (巴黎萨克雷大学)
关键词: recently proposed Regensburg, proposed Regensburg Breast, Regensburg Breast Shape, proposed Regensburg, Regensburg Breast
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages, 5 figures

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Abstract:We present the first deep implicit 3D shape model of the female breast, building upon and improving the recently proposed Regensburg Breast Shape Model (RBSM). Compared to its PCA-based predecessor, our model employs implicit neural representations; hence, it can be trained on raw 3D breast scans and eliminates the need for computationally demanding non-rigid registration – a task that is particularly difficult for feature-less breast shapes. The resulting model, dubbed iRBSM, captures detailed surface geometry including fine structures such as nipples and belly buttons, is highly expressive, and outperforms the RBSM on different surface reconstruction tasks. Finally, leveraging the iRBSM, we present a prototype application to 3D reconstruct breast shapes from just a single image. Model and code publicly available at this https URL.
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[CV-101] ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home Rearrangement Tasks

【速读】：该论文试图解决机器人领域中低级操作和家庭物体重新排列任务的高质量基准测试问题。解决方案的关键在于提出了MS-HAB基准，包括以下几个核心要素：首先，通过GPU加速实现了Home Assistant Benchmark (HAB)，支持现实低级控制，并在相似的GPU内存使用下实现了比之前魔法抓取实现快3倍的速度；其次，训练了广泛的强化学习 (Reinforcement Learning, RL) 和模仿学习 (Imitation Learning, IL) 基线，为未来工作提供比较基础；最后，开发了基于规则的轨迹过滤系统，从RL策略中采样符合预定义机器人行为和安全标准的演示，结合快速环境实现大规模高效、可控的数据生成。

链接: https://arxiv.org/abs/2412.13211
作者: Arth Shukla,Stone Tao,Hao Su
机构: Hillbot Inc.; University of California, San Diego (加州大学圣地亚哥分校)
关键词: enabling significant advancements, High-quality benchmarks, embodied AI research, enabling significant, long-horizon navigation
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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Abstract:High-quality benchmarks are the foundation for embodied AI research, enabling significant advancements in long-horizon navigation, manipulation and rearrangement tasks. However, as frontier tasks in robotics get more advanced, they require faster simulation speed, more intricate test environments, and larger demonstration datasets. To this end, we present MS-HAB, a holistic benchmark for low-level manipulation and in-home object rearrangement. First, we provide a GPU-accelerated implementation of the Home Assistant Benchmark (HAB). We support realistic low-level control and achieve over 3x the speed of previous magical grasp implementations at similar GPU memory usage. Second, we train extensive reinforcement learning (RL) and imitation learning (IL) baselines for future work to compare against. Finally, we develop a rule-based trajectory filtering system to sample specific demonstrations from our RL policies which match predefined criteria for robot behavior and safety. Combining demonstration filtering with our fast environments enables efficient, controlled data generation at scale.
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[CV-102] Parameter-efficient Fine-tuning for improved Convolutional Baseline for Brain Tumor Segmentation in Sub-Saharan Africa Adult Glioma Dataset MICCAI2024

【速读】：该论文试图解决脑肿瘤分割中存在的领域迁移问题和低资源环境下的数据稀缺问题。解决方案的关键在于提出了基于卷积适配器启发的参数高效微调（PEFT）方法，对MedNeXt架构进行微调。通过在BraTS-2021数据集上进行预训练并在BraTS-Africa数据集上进行微调，PEFT方法在减少训练计算量的同时，实现了与全量微调相当的分割性能（平均Dice系数为0.8），并且在BraTS-Africa数据集上的表现显著优于仅在BraTS-2021数据集上训练的模型。尽管PEFT方法在某些情况下可能存在过分割的倾向（高特异性0.99，低敏感性0.75），但其整体性能与全量微调相当，且具有更低的性能方差。

链接: https://arxiv.org/abs/2412.14100
作者: Bijay Adhikari,Pratibha Kulung,Jakesh Bohaju,Laxmi Kanta Poudel,Confidence Raymond,Dong Zhang,Udunna C Anazodo,Bishesh Khanal,Mahesh Shakya
机构: 未知
关键词: Automating brain tumor, brain tumor segmentation, Automating brain, deep learning methods, medical imaging
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted to “The International Brain Tumor Segmentation (BraTS) challenge organized at MICCAI 2024 conference”

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Abstract:Automating brain tumor segmentation using deep learning methods is an ongoing challenge in medical imaging. Multiple lingering issues exist including domain-shift and applications in low-resource settings which brings a unique set of challenges including scarcity of data. As a step towards solving these specific problems, we propose Convolutional adapter-inspired Parameter-efficient Fine-tuning (PEFT) of MedNeXt architecture. To validate our idea, we show our method performs comparable to full fine-tuning with the added benefit of reduced training compute using BraTS-2021 as pre-training dataset and BraTS-Africa as the fine-tuning dataset. BraTS-Africa consists of a small dataset (60 train / 35 validation) from the Sub-Saharan African population with marked shift in the MRI quality compared to BraTS-2021 (1251 train samples). We first show that models trained on BraTS-2021 dataset do not generalize well to BraTS-Africa as shown by 20% reduction in mean dice on BraTS-Africa validation samples. Then, we show that PEFT can leverage both the BraTS-2021 and BraTS-Africa dataset to obtain mean dice of 0.8 compared to 0.72 when trained only on BraTS-Africa. Finally, We show that PEFT (0.80 mean dice) results in comparable performance to full fine-tuning (0.77 mean dice) which may show PEFT to be better on average but the boxplots show that full finetuning results is much lesser variance in performance. Nevertheless, on disaggregation of the dice metrics, we find that the model has tendency to oversegment as shown by high specificity (0.99) compared to relatively low sensitivity(0.75). The source code is available at this https URL
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[CV-103] Diagnosising Helicobacter pylori using AutoEncoders and Limited Annotations through Anomalous Staining Patterns in IHC Whole Slide Images

【速读】：该论文旨在解决幽门螺杆菌（Helicobacter pylori, H. pylori）在组织学图像中的检测问题，特别是通过免疫组化染色图像进行检测。由于这一分析过程耗时且依赖于专家病理学家的视觉检查，论文提出了一种基于有限标注数据的解决方案。关键在于使用自编码器（autoencoders）学习健康区域的潜在模式，并通过在HSV空间中的重建误差来量化异常染色区域。通过ROC分析确定最佳阈值，并结合阳性区域的百分比来判断H. pylori的存在。实验结果表明，该方法在仅使用163个阳性标注的情况下，实现了91%的准确率、86%的敏感性、96%的特异性和0.97的AUC，展示了其在有限标注数据下的竞争性性能。

链接: https://arxiv.org/abs/2412.13857
作者: Pau Cano,Eva Musulen,Debora Gil
机构: 未知
关键词: Helicobacter pylori, detection of Helicobacter, work addresses, histological images, Purpose
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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Abstract:Purpose: This work addresses the detection of Helicobacter pylori (H. pylori) in histological images with immunohistochemical staining. This analysis is a time demanding task, currently done by an expert pathologist that visually inspects the samples. Given the effort required to localise the pathogen in images, a limited number of annotations might be available in an initial setting. Our goal is to design an approach that, using a limited set of annotations, is capable of obtaining results good enough to be used as a support tool. Methods: We propose to use autoencoders to learn the latent patterns of healthy patches and formulate a specific measure of the reconstruction error of the image in HSV space. ROC analysis is used to set the optimal threshold of this measure and the percentage of positive patches in a sample that determines the presence of H. pylori. Results: Our method has been tested on an own database of 245 Whole Slide Images (WSI) having 117 cases without H. pylori and different density of the bacteria in the remaining ones. The database has 1211 annotated patches, with only 163 positive patches. This dataset of positive annotations was used to train a baseline thresholding and an SVM using the features of a pre-trained RedNet18 and ViT models. A 10-fold cross-validation shows that our method has better performance with 91% accuracy, 86% sensitivity, 96% specificity and 0.97 AUC in the diagnosis of H. pylori. Conclusion: Unlike classification approaches, our shallow autoencoder with threshold adaptation for the detection of anomalous staining is able to achieve competitive results with a limited set of annotated data. This initial approach is good enough to be used as a guide for fast annotation of infected patches.
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[CV-104] Spatial Brain Tumor Concentration Estimation for Individualized Radiotherapy Planning

【速读】：该论文试图解决脑肿瘤个性化放射治疗规划中肿瘤细胞分布的估计问题，尤其是现有方法计算量大、难以广泛应用于临床实践的挑战。解决方案的关键在于提出了一种高效且直接的方法，通过软物理约束从术前MRI中估计肿瘤细胞浓度。该方法通过同时最小化观测MRI与物理信息损失函数之间的差异，优化三维肿瘤浓度场，显著提高了肿瘤复发的预测准确性，并在两个公开数据集上验证了其有效性。与现有最先进方法相比，该方法在保持临床可行的运行时间（不到一分钟）的同时，大幅减少了计算时间（从30分钟降至一分钟），并展示了其通过整合额外影像信息和物理约束来适应不同医学扩散现象的潜力。

链接: https://arxiv.org/abs/2412.13811
作者: Jonas Weidner,Michal Balcerak,Ivan Ezhov,André Datchev,Laurin Lux,Lucas Zimmerand Daniel Rueckert,Björn Menze,Benedikt Wiestler
机构: 未知
关键词: personalizing radiotherapy planning, Biophysical modeling, promising strategy, strategy for personalizing, personalizing radiotherapy
类目: Medical Physics (physics.med-ph); Computer Vision and Pattern Recognition (cs.CV)
备注:

点击查看摘要

Abstract:Biophysical modeling of brain tumors has emerged as a promising strategy for personalizing radiotherapy planning by estimating the otherwise hidden distribution of tumor cells within the brain. However, many existing state-of-the-art methods are computationally intensive, limiting their widespread translation into clinical practice. In this work, we propose an efficient and direct method that utilizes soft physical constraints to estimate the tumor cell concentration from preoperative MRI of brain tumor patients. Our approach optimizes a 3D tumor concentration field by simultaneously minimizing the difference between the observed MRI and a physically informed loss function. Compared to existing state-of-the-art techniques, our method significantly improves predicting tumor recurrence on two public datasets with a total of 192 patients while maintaining a clinically viable runtime of under one minute - a substantial reduction from the 30 minutes required by the current best approach. Furthermore, we showcase the generalizability of our framework by incorporating additional imaging information and physical constraints, highlighting its potential to translate to various medical diffusion phenomena with imperfect data.
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[CV-105] MBInception: A new Multi-Block Inception Model for Enhancing Image Processing Efficiency

【速读】：该论文试图解决图像分类中的性能提升问题，解决方案的关键在于引入了一种创新的卷积神经网络模型，该模型通过三个连续的Inception模块（inception blocks）来增强特征提取能力。与现有的Visual Geometry Group (VGG)、Residual Network (ResNet) 和 MobileNet 等架构相比，该模型在多个基准数据集（如CIFAR、MNIST和Fashion-MNIST）上表现出更优的分类性能，从而有效推动了图像分类领域的技术进步。

链接: https://arxiv.org/abs/2412.13703
作者: Fatemeh Froughirad,Reza Bakhoda Eshtivani,Hamed Khajavi,Amir Rastgoo
机构: 未知
关键词: Deep learning models, raw pixel data, convolutional neural networks, autonomously extracting features, extracting features directly
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Numerical Analysis (math.NA)
备注: 26 pages, 10 figures

点击查看摘要

Abstract:Deep learning models, specifically convolutional neural networks, have transformed the landscape of image classification by autonomously extracting features directly from raw pixel data. This article introduces an innovative image classification model that employs three consecutive inception blocks within a convolutional neural networks framework, providing a comprehensive comparative analysis with well-established architectures such as Visual Geometry Group, Residual Network, and MobileNet. Through the utilization of benchmark datasets, including Canadian Institute for Advanced Researc, Modified National Institute of Standards and Technology database, and Fashion Modified National Institute of Standards and Technology database, we assess the performance of our proposed model in comparison to these benchmarks. The outcomes reveal that our novel model consistently outperforms its counterparts across diverse datasets, underscoring its effectiveness and potential for advancing the current state-of-the-art in image classification. Evaluation metrics further emphasize that the proposed model surpasses the other compared architectures, thereby enhancing the efficiency of image classification on standard datasets.
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[CV-106] Plug-and-Play Tri-Branch Invertible Block for Image Rescaling AAAI2025

【速读】：该论文试图解决传统图像缩放方法在处理低频信息时引入的通道冗余问题，以及在高频信息建模时依赖特定分布的局限性。解决方案的关键在于提出了一种可插拔的三分支可逆块 (T-InvBlocks)，通过将低频分支分解为亮度 (Y) 和色度 (CbCr) 分量来减少冗余，并采用全零映射策略处理高频信息，从而在缩放过程中更高效地保留关键信息。这一方法能够无缝集成到现有缩放模型中，显著提升高分辨率图像重建的性能，特别是在涉及有损压缩的场景中。

链接: https://arxiv.org/abs/2412.13508
作者: Jingwei Bao,Jinhua Hao,Pengcheng Xu,Ming Sun,Chao Zhou,Shuyuan Zhu
机构: 未知
关键词: downscaled to low-resolution, reduce bandwidth, original details, commonly downscaled, restore their original
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by AAAI 2025. Code is available at this https URL

点击查看摘要

Abstract:High-resolution (HR) images are commonly downscaled to low-resolution (LR) to reduce bandwidth, followed by upscaling to restore their original details. Recent advancements in image rescaling algorithms have employed invertible neural networks (INNs) to create a unified framework for downscaling and upscaling, ensuring a one-to-one mapping between LR and HR images. Traditional methods, utilizing dual-branch based vanilla invertible blocks, process high-frequency and low-frequency information separately, often relying on specific distributions to model high-frequency components. However, processing the low-frequency component directly in the RGB domain introduces channel redundancy, limiting the efficiency of image reconstruction. To address these challenges, we propose a plug-and-play tri-branch invertible block (T-InvBlocks) that decomposes the low-frequency branch into luminance (Y) and chrominance (CbCr) components, reducing redundancy and enhancing feature processing. Additionally, we adopt an all-zero mapping strategy for high-frequency components during upscaling, focusing essential rescaling information within the LR image. Our T-InvBlocks can be seamlessly integrated into existing rescaling models, improving performance in both general rescaling tasks and scenarios involving lossy compression. Extensive experiments confirm that our method advances the state of the art in HR image reconstruction.
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[CV-107] Generating Unseen Nonlinear Evolution in Sea Surface Temperature Using a Deep Learning-Based Latent Space Data Assimilation Framework

【速读】：该论文试图解决在地球系统预测中，如何有效融合多源数据并重建观测中缺失的非线性演化过程的问题。解决方案的关键在于设计了一个基于数据驱动的潜在空间数据同化框架 (DeepDA)，该框架利用生成式 AI (Generative AI) 模型捕捉海表温度的非线性演化。通过在变分约束下嵌入非线性特征，DeepDA 能够有效融合异质数据，并在观测信息大量缺失的情况下保持高稳定性，生成非线性演化。此外，论文还从物理模式的角度分析了 DeepDA 生成的非线性演化，揭示了其在捕捉多尺度海洋信号方面的内在可解释性。

链接: https://arxiv.org/abs/2412.13477
作者: Qingyu Zheng,Guijun Han,Wei Li,Lige Cao,Gongfu Zhou,Haowen Wu,Qi Shao,Ru Wang,Xiaobo Wu,Xudong Cui,Hong Li,Xuan Wang
机构: 未知
关键词: Earth system predictions, accuracy of Earth, Earth system, system predictions, greatly improved
类目: Atmospheric and Oceanic Physics (physics.ao-ph); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Geophysics (physics.geo-ph)
备注: 31 pages, 14 figures

点击查看摘要

Abstract:Advances in data assimilation (DA) methods have greatly improved the accuracy of Earth system predictions. To fuse multi-source data and reconstruct the nonlinear evolution missing from observations, geoscientists are developing future-oriented DA methods. In this paper, we redesign a purely data-driven latent space DA framework (DeepDA) that employs a generative artificial intelligence model to capture the nonlinear evolution in sea surface temperature. Under variational constraints, DeepDA embedded with nonlinear features can effectively fuse heterogeneous data. The results show that DeepDA remains highly stable in capturing and generating nonlinear evolutions even when a large amount of observational information is missing. It can be found that when only 10% of the observation information is available, the error increase of DeepDA does not exceed 40%. Furthermore, DeepDA has been shown to be robust in the fusion of real observations and ensemble simulations. In particular, this paper provides a mechanism analysis of the nonlinear evolution generated by DeepDA from the perspective of physical patterns, which reveals the inherent explainability of our DL model in capturing multi-scale ocean signals.
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[CV-108] In-context learning for medical image segmentation

【速读】：该论文试图解决医学图像标注（Annotation of medical images）中由于专业人员工作量大而导致的标注瓶颈问题，特别是在MRI和CT扫描图像的分割任务中。解决方案的关键是提出了上下文级联分割（In-context Cascade Segmentation, ICS）方法，该方法基于UniverSeg框架，通过少样本分割（few-shot segmentation）技术，利用支持图像（support images）进行分割而不需要额外的训练。ICS通过迭代地将每个切片的推理结果添加到支持集中，实现了序列图像的前向和后向信息传播，确保了切片间的边界一致性（inter-slice consistency）。实验结果表明，ICS在复杂解剖区域的分割性能显著优于基线方法，特别是在维持切片间边界一致性方面。此外，ICS通过减少初始支持切片（initial support slices）的数量和位置对分割精度的影响，进一步降低了标注负担，为临床和研究应用提供了强有力的支持。

链接: https://arxiv.org/abs/2412.13299
作者: Eichi Takaya,Shinnosuke Yamamoto
机构: Tohoku University Hospital(东北大学医院); Tohoku University Graduate School of Medicine(东北大学医学研究生院)
关键词: evaluating treatment efficacy, planning radiotherapy, crucial for evaluating, evaluating treatment, treatment efficacy
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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点击查看摘要

Abstract:Annotation of medical images, such as MRI and CT scans, is crucial for evaluating treatment efficacy and planning radiotherapy. However, the extensive workload of medical professionals limits their ability to annotate large image datasets, posing a bottleneck for AI applications in medical imaging. To address this, we propose In-context Cascade Segmentation (ICS), a novel method that minimizes annotation requirements while achieving high segmentation accuracy for sequential medical images. ICS builds on the UniverSeg framework, which performs few-shot segmentation using support images without additional training. By iteratively adding the inference results of each slice to the support set, ICS propagates information forward and backward through the sequence, ensuring inter-slice consistency. We evaluate the proposed method on the HVSMR dataset, which includes segmentation tasks for eight cardiac regions. Experimental results demonstrate that ICS significantly improves segmentation performance in complex anatomical regions, particularly in maintaining boundary consistency across slices, compared to baseline methods. The study also highlights the impact of the number and position of initial support slices on segmentation accuracy. ICS offers a promising solution for reducing annotation burdens while delivering robust segmentation results, paving the way for its broader adoption in clinical and research applications.
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[CV-109] Optimized two-stage AI-based Neural Decoding for Enhanced Visual Stimulus Reconstruction from fMRI Data

【速读】：该论文试图解决从功能性磁共振成像 (fMRI) 数据中重建视觉感知的问题，特别是在处理复杂和噪声较大的 fMRI 数据时，如何提高重建图像的结构相似性和语义准确性。解决方案的关键在于提出了一种非线性深度网络，用于优化 fMRI 数据的潜在空间表示，并通过两阶段的生成式 AI (Generative AI) 方法来逐步提升重建图像的质量。第一阶段提供粗略的视觉近似，第二阶段利用潜在扩散模型 (LDM) 和 CLIP 嵌入来改进刺激预测。实验结果表明，该方法在结构相似性和感知相似性方面均优于传统的基于岭回归线性变换的模型。

链接: https://arxiv.org/abs/2412.13237
作者: Lorenzo Veronese,Andrea Moglia,Luca Mainardi,Pietro Cerveri
机构: Politecnico di Milano(米兰理工大学); Università di Pavia(帕维亚大学)
关键词: AI-based neural decoding, map brain activity, neural decoding reconstructs, decoding reconstructs visual, reconstructs visual perception
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
备注: 14 pages, 5 figures

点击查看摘要

Abstract:AI-based neural decoding reconstructs visual perception by leveraging generative models to map brain activity, measured through functional MRI (fMRI), into latent hierarchical representations. Traditionally, ridge linear models transform fMRI into a latent space, which is then decoded using latent diffusion models (LDM) via a pre-trained variational autoencoder (VAE). Due to the complexity and noisiness of fMRI data, newer approaches split the reconstruction into two sequential steps, the first one providing a rough visual approximation, the second on improving the stimulus prediction via LDM endowed by CLIP embeddings. This work proposes a non-linear deep network to improve fMRI latent space representation, optimizing the dimensionality alike. Experiments on the Natural Scenes Dataset showed that the proposed architecture improved the structural similarity of the reconstructed image by about 2% with respect to the state-of-the-art model, based on ridge linear transform. The reconstructed image’s semantics improved by about 4%, measured by perceptual similarity, with respect to the state-of-the-art. The noise sensitivity analysis of the LDM showed that the role of the first stage was fundamental to predict the stimulus featuring high structural similarity. Conversely, providing a large noise stimulus affected less the semantics of the predicted stimulus, while the structural similarity between the ground truth and predicted stimulus was very poor. The findings underscore the importance of leveraging non-linear relationships between BOLD signal and the latent representation and two-stage generative AI for optimizing the fidelity of reconstructed visual stimuli from noisy fMRI data.
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人工智能

[AI-0] Advanced Reasoning and Transformation Engine for Multi-Step Insight Synthesis in Data Analytics with Large Language Models

链接: https://arxiv.org/abs/2412.14146
作者: Atin Sakkeer Hussain
关键词: Large Language Models, augment Large Language, Language Models, Large Language, Multi-Step Insight Synthesis
类目: Artificial Intelligence (cs.AI); Databases (cs.DB)
*备注:

点击查看摘要

Abstract:This paper presents the Advanced Reasoning and Transformation Engine for Multi-Step Insight Synthesis in Data Analytics (ARTEMIS-DA), a novel framework designed to augment Large Language Models (LLMs) for solving complex, multi-step data analytics tasks. ARTEMIS-DA integrates three core components: the Planner, which dissects complex user queries into structured, sequential instructions encompassing data preprocessing, transformation, predictive modeling, and visualization; the Coder, which dynamically generates and executes Python code to implement these instructions; and the Grapher, which interprets generated visualizations to derive actionable insights. By orchestrating the collaboration between these components, ARTEMIS-DA effectively manages sophisticated analytical workflows involving advanced reasoning, multi-step transformations, and synthesis across diverse data modalities. The framework achieves state-of-the-art (SOTA) performance on benchmarks such as WikiTableQuestions and TabFact, demonstrating its ability to tackle intricate analytical tasks with precision and adaptability. By combining the reasoning capabilities of LLMs with automated code generation and execution and visual analysis, ARTEMIS-DA offers a robust, scalable solution for multi-step insight synthesis, addressing a wide range of challenges in data analytics.

[AI-1] LLM s can realize combinatorial creativity: generating creative ideas via LLM s for scientific research

链接: https://arxiv.org/abs/2412.14141
作者: Tianyang Gu,Jingjin Wang,Zhihao Zhang,HaoHong Li
关键词: implementing creative processes, Scientific idea generation, Large Language Models, Scientific idea, providing valuable frameworks
类目: Artificial Intelligence (cs.AI)
*备注:

点击查看摘要

Abstract:Scientific idea generation has been extensively studied in creativity theory and computational creativity research, providing valuable frameworks for understanding and implementing creative processes. However, recent work using Large Language Models (LLMs) for research idea generation often overlooks these theoretical foundations. We present a framework that explicitly implements combinatorial creativity theory using LLMs, featuring a generalization-level retrieval system for cross-domain knowledge discovery and a structured combinatorial process for idea generation. The retrieval system maps concepts across different abstraction levels to enable meaningful connections between disparate domains, while the combinatorial process systematically analyzes and recombines components to generate novel solutions. Experiments on the OAG-Bench dataset demonstrate our framework’s effectiveness, consistently outperforming baseline approaches in generating ideas that align with real research developments (improving similarity scores by 7%-10% across multiple metrics). Our results provide strong evidence that LLMs can effectively realize combinatorial creativity when guided by appropriate theoretical frameworks, contributing both to practical advancement of AI-assisted research and theoretical understanding of machine creativity.

[AI-2] Design choices made by LLM -based test generators prevent them from finding bugs

链接: https://arxiv.org/abs/2412.14137
作者: Noble Saji Mathews,Meiyappan Nagappan
关键词: Large Language Models, Language Models, Large Language, test generation tools, automated test case
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
*备注:

点击查看摘要

Abstract:There is an increasing amount of research and commercial tools for automated test case generation using Large Language Models (LLMs). This paper critically examines whether recent LLM-based test generation tools, such as Codium CoverAgent and CoverUp, can effectively find bugs or unintentionally validate faulty code. Considering bugs are only exposed by failing test cases, we explore the question: can these tools truly achieve the intended objectives of software testing when their test oracles are designed to pass? Using real human-written buggy code as input, we evaluate these tools, showing how LLM-generated tests can fail to detect bugs and, more alarmingly, how their design can worsen the situation by validating bugs in the generated test suite and rejecting bug-revealing tests. These findings raise important questions about the validity of the design behind LLM-based test generation tools and their impact on software quality and test suite reliability.

[AI-3] Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective

链接: https://arxiv.org/abs/2412.14135
作者: Zhiyuan Zeng,Qinyuan Cheng,Zhangyue Yin,Bo Wang,Shimin Li,Yunhua Zhou,Qipeng Guo,Xuanjing Huang,Xipeng Qiu
关键词: Artificial Inteiligence, require strong reasoning, milestone in Artificial, http URL, URL has claimed
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:OpenAI o1 represents a significant milestone in Artificial Inteiligence, which achieves expert-level performances on many challanging tasks that require strong reasoning this http URL has claimed that the main techinique behinds o1 is the reinforcement learining. Recent works use alternative approaches like knowledge distillation to imitate o1’s reasoning style, but their effectiveness is limited by the capability ceiling of the teacher model. Therefore, this paper analyzes the roadmap to achieving o1 from the perspective of reinforcement learning, focusing on four key components: policy initialization, reward design, search, and learning. Policy initialization enables models to develop human-like reasoning behaviors, equipping them with the ability to effectively explore solution spaces for complex problems. Reward design provides dense and effective signals via reward shaping or reward modeling, which is the guidance for both search and learning. Search plays a crucial role in generating high-quality solutions during both training and testing phases, which can produce better solutions with more computation. Learning utilizes the data generated by search for improving policy, which can achieve the better performance with more parameters and more searched data. Existing open-source projects that attempt to reproduce o1 can be seem as a part or a variant of our roadmap. Collectively, these components underscore how learning and search drive o1’s advancement, making meaningful contributions to the development of LLM.

[AI-4] Future Research Avenues for Artificial Intelligence in Digital Gaming: An Exploratory Report

链接: https://arxiv.org/abs/2412.14085
作者: Markus Dablander
关键词: synergistic application domain, enhance player experience, providing valuable benchmarks, artificial intelligence, experience and immersion
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
*备注:

点击查看摘要

Abstract:Video games are a natural and synergistic application domain for artificial intelligence (AI) systems, offering both the potential to enhance player experience and immersion, as well as providing valuable benchmarks and virtual environments to advance AI technologies in general. This report presents a high-level overview of five promising research pathways for applying state-of-the-art AI methods, particularly deep learning, to digital gaming within the context of the current research landscape. The objective of this work is to outline a curated, non-exhaustive list of encouraging research directions at the intersection of AI and video games that may serve to inspire more rigorous and comprehensive research efforts in the future. We discuss (i) investigating large language models as core engines for game agent modelling, (ii) using neural cellular automata for procedural game content generation, (iii) accelerating computationally expensive in-game simulations via deep surrogate modelling, (iv) leveraging self-supervised learning to obtain useful video game state embeddings, and (v) training generative models of interactive worlds using unlabelled video data. We also briefly address current technical challenges associated with the integration of advanced deep learning systems into video game development, and indicate key areas where further progress is likely to be beneficial.

[AI-5] Dialogue with the Machine and Dialogue with the Art World: Evaluating Generative AI for Culturally-Situated Creativity NEURIPS2024

链接: https://arxiv.org/abs/2412.14077
作者: Rida Qadri,Piotr Mirowski,Aroussiak Gabriellan,Farbod Mehr,Huma Gupta,Pamela Karimi,Remi Denton
关键词: Art Worlds, paper proposes dialogue, culturally-situated creative practice, socially situated nature, sociologist Howard Becker
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
*备注: NeurIPS 2024 Creative AI Track

点击查看摘要

Abstract:This paper proposes dialogue as a method for evaluating generative AI tools for culturally-situated creative practice, that recognizes the socially situated nature of art. Drawing on sociologist Howard Becker’s concept of Art Worlds, this method expands the scope of traditional AI and creativity evaluations beyond benchmarks, user studies with crowd-workers, or focus groups conducted with artists. Our method involves two mutually informed dialogues: 1) ‘dialogues with art worlds’ placing artists in conversation with experts such as art historians, curators, and archivists, and 2)‘dialogues with the machine,’ facilitated through structured artist- and critic-led experimentation with state-of-the-art generative AI tools. We demonstrate the value of this method through a case study with artists and experts steeped in non-western art worlds, specifically the Persian Gulf. We trace how these dialogues help create culturally rich and situated forms of evaluation for representational possibilities of generative AI that mimic the reception of generative artwork in the broader art ecosystem. Putting artists in conversation with commentators also allow artists to shift their use of the tools to respond to their cultural and creative context. Our study can provide generative AI researchers an understanding of the complex dynamics of technology, human creativity and the socio-politics of art worlds, to build more inclusive machines for diverse art worlds.

[AI-6] A Computationally Grounded Framework for Cognitive Attitudes (extended version)

链接: https://arxiv.org/abs/2412.14073
作者: Tiago de Lima,Emiliano Lorini,Elise Perrotin,François Schwarzentruber
关键词: agents’ cognitive attitudes, agents’ cognitive, cognitive attitudes, epistemic and motivational, motivational type
类目: Artificial Intelligence (cs.AI)
*备注:

点击查看摘要

Abstract:We introduce a novel language for reasoning about agents’ cognitive attitudes of both epistemic and motivational type. We interpret it by means of a computationally grounded semantics using belief bases. Our language includes five types of modal operators for implicit belief, complete attraction, complete repulsion, realistic attraction and realistic repulsion. We give an axiomatization and show that our operators are not mutually expressible and that they can be combined to represent a large variety of psychological concepts including ambivalence, indifference, being motivated, being demotivated and preference. We present a dynamic extension of the language that supports reasoning about the effects of belief change operations. Finally, we provide a succinct formulation of model checking for our languages and a PSPACE model checking algorithm relying on a reduction into TQBF. We present some experimental results for the implemented algorithm on computation time in a concrete example.

[AI-7] Rango: Adaptive Retrieval-Augmented Proving for Automated Software Verification ICSE

链接: https://arxiv.org/abs/2412.14063
作者: Kyle Thompson,Nuno Saavedra,Pedro Carrott,Kevin Fisher,Alex Sanchez-Stern,Yuriy Brun,João F. Ferreira,Sorin Lerner,Emily First
关键词: Formal verification, enables the creation, high-quality software, creation of high-quality, Formal
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
*备注: In Proceedings of the 47th International Conference on Software Engineering (ICSE), Ottawa, ON, Canada, April 2025

点击查看摘要

Abstract:Formal verification using proof assistants, such as Coq, enables the creation of high-quality software. However, the verification process requires significant expertise and manual effort to write proofs. Recent work has explored automating proof synthesis using machine learning and large language models (LLMs). This work has shown that identifying relevant premises, such as lemmas and definitions, can aid synthesis. We present Rango, a fully automated proof synthesis tool for Coq that automatically identifies relevant premises and also similar proofs from the current project and uses them during synthesis. Rango uses retrieval augmentation at every step of the proof to automatically determine which proofs and premises to include in the context of its fine-tuned LLM. In this way, Rango adapts to the project and to the evolving state of the proof. We create a new dataset, CoqStoq, of 2,226 open-source Coq projects and 196,929 theorems from GitHub, which includes both training data and a curated evaluation benchmark of well-maintained projects. On this benchmark, Rango synthesizes proofs for 32.0% of the theorems, which is 29% more theorems than the prior state-of-the-art tool Tactician. Our evaluation also shows that Rango adding relevant proofs to its context leads to a 47% increase in the number of theorems proven.

[AI-8] Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problems AAAI-25 AAAI

链接: https://arxiv.org/abs/2412.14052
作者: Igor G. Smit,Yaoxin Wu,Pavel Troubil,Yingqian Zhang,Wim P.M. Nuijten
关键词: solve combinatorial optimization, efficiently solve combinatorial, Neural combinatorial optimization, combinatorial optimization problems, combinatorial optimization
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: Accepted by the 39th Annual AAAI Conference on Artificial Intelligence (AAAI-25)

点击查看摘要

Abstract:Neural combinatorial optimization (NCO) has gained significant attention due to the potential of deep learning to efficiently solve combinatorial optimization problems. NCO has been widely applied to job shop scheduling problems (JSPs) with the current focus predominantly on deterministic problems. In this paper, we propose a novel attention-based scenario processing module (SPM) to extend NCO methods for solving stochastic JSPs. Our approach explicitly incorporates stochastic information by an attention mechanism that captures the embedding of sampled scenarios (i.e., an approximation of stochasticity). Fed with the embedding, the base neural network is intervened by the attended scenarios, which accordingly learns an effective policy under stochasticity. We also propose a training paradigm that works harmoniously with either the expected makespan or Value-at-Risk objective. Results demonstrate that our approach outperforms existing learning and non-learning methods for the flexible JSP problem with stochastic processing times on a variety of instances. In addition, our approach holds significant generalizability to varied numbers of scenarios and disparate distributions.

[AI-9] Landscape of AI safety concerns - A methodology to support safety assurance for AI-based autonomous systems

链接: https://arxiv.org/abs/2412.14020
作者: Ronald Schnitzer,Lennart Kilian,Simon Roessner,Konstantinos Theodorou,Sonja Zillner
关键词: Artificial Intelligence, safety concerns, key technology, driving advancements, safety
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
*备注:

点击查看摘要

Abstract:Artificial Intelligence (AI) has emerged as a key technology, driving advancements across a range of applications. Its integration into modern autonomous systems requires assuring safety. However, the challenge of assuring safety in systems that incorporate AI components is substantial. The lack of concrete specifications, and also the complexity of both the operational environment and the system itself, leads to various aspects of uncertain behavior and complicates the derivation of convincing evidence for system safety. Nonetheless, scholars proposed to thoroughly analyze and mitigate AI-specific insufficiencies, so-called AI safety concerns, which yields essential evidence supporting a convincing assurance case. In this paper, we build upon this idea and propose the so-called Landscape of AI Safety Concerns, a novel methodology designed to support the creation of safety assurance cases for AI-based systems by systematically demonstrating the absence of AI safety concerns. The methodology’s application is illustrated through a case study involving a driverless regional train, demonstrating its practicality and effectiveness.

[AI-10] Discovering maximally consistent distribution of causal tournaments with Large Language Models

链接: https://arxiv.org/abs/2412.14019
作者: Federico Baldo,Simon Ferreira,Charles K. Assaad
关键词: understanding complex systems, Large Language Models, untestable assumptions, complex systems, depend on strong
类目: Artificial Intelligence (cs.AI)
*备注:

点击查看摘要

Abstract:Causal discovery is essential for understanding complex systems, yet traditional methods often depend on strong, untestable assumptions, making the process challenging. Large Language Models (LLMs) present a promising alternative for extracting causal insights from text-based metadata, which consolidates domain expertise. However, LLMs are prone to unreliability and hallucinations, necessitating strategies that account for their limitations. One such strategy involves leveraging a consistency measure to evaluate reliability. Additionally, most text metadata does not clearly distinguish direct causal relationships from indirect ones, further complicating the inference of causal graphs. As a result, focusing on causal orderings, rather than causal graphs, emerges as a more practical and robust approach. We propose a novel method to derive a distribution of acyclic tournaments (representing plausible causal orders) that maximizes a consistency score. Our approach begins by computing pairwise consistency scores between variables, yielding a cyclic tournament that aggregates these scores. From this structure, we identify optimal acyclic tournaments compatible with the original tournament, prioritizing those that maximize consistency across all configurations. We tested our method on both classical and well-established bechmarks, as well as real-world datasets from epidemiology and public health. Our results demonstrate the effectiveness of our approach in recovering distributions causal orders with minimal error.

[AI-11] Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes

链接: https://arxiv.org/abs/2412.13998
作者: Katarzyna Kobalczyk,Claudio Fanconi,Hao Sun,Mihaela van der Schaar
关键词: large language models, everyday applications, critical challenge, large language, increasingly embedded
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
*备注:

点击查看摘要

Abstract:As large language models (LLMs) become increasingly embedded in everyday applications, ensuring their alignment with the diverse preferences of individual users has become a critical challenge. Currently deployed approaches typically assume homogeneous user objectives and rely on single-objective fine-tuning. However, human preferences are inherently heterogeneous, influenced by various unobservable factors, leading to conflicting signals in preference data. Existing solutions addressing this diversity often require costly datasets labelled for specific objectives and involve training multiple reward models or LLM policies, which is computationally expensive and impractical. In this work, we present a novel framework for few-shot steerable alignment, where users’ underlying preferences are inferred from a small sample of their choices. To achieve this, we extend the Bradley-Terry-Luce model to handle heterogeneous preferences with unobserved variability factors and propose its practical implementation for reward modelling and LLM fine-tuning. Thanks to our proposed approach of functional parameter-space conditioning, LLMs trained with our framework can be adapted to individual preferences at inference time, generating outputs over a continuum of behavioural modes. We empirically validate the effectiveness of methods, demonstrating their ability to capture and align with diverse human preferences in a data-efficient manner. Our code is made available at: this https URL.

[AI-12] DODGE: Ontology-Aware Risk Assessment via Object-Oriented Disruption Graphs

链接: https://arxiv.org/abs/2412.13964
作者: Stefano M. Nicoletti,E. Moritz Hahn,Mattia Fumagalli,Giancarlo Guizzardi,Mariëlle Stoelinga
关键词: functional firewall mitigates, flat tyre, intruding the network, risk, charged battery
类目: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
*备注:

点击查看摘要

Abstract:When considering risky events or actions, we must not downplay the role of involved objects: a charged battery in our phone averts the risk of being stranded in the desert after a flat tyre, and a functional firewall mitigates the risk of a hacker intruding the network. The Common Ontology of Value and Risk (COVER) highlights how the role of objects and their relationships remains pivotal to performing transparent, complete and accountable risk assessment. In this paper, we operationalize some of the notions proposed by COVER - such as parthood between objects and participation of objects in events/actions - by presenting a new framework for risk assessment: DODGE. DODGE enriches the expressivity of vetted formal models for risk - i.e., fault trees and at- tack trees - by bridging the disciplines of ontology and formal methods into an ontology-aware formal framework composed by a more expressive modelling formalism, Object-Oriented Disruption Graphs (ODGs), logic (ODGLog) and an intermediate query language (ODGLang). With these, DODGE allows risk assessors to pose questions about disruption propagation, disruption likelihood and risk levels, keeping the fundamental role of objects at risk always in sight.

[AI-13] hreshold UCT: Cost-Constrained Monte Carlo Tree Search with Pareto Curves

链接: https://arxiv.org/abs/2412.13962
作者: Martin Kurečka,Václav Nevyhoštěný,Petr Novotný,Vít Unčovský
关键词: Constrained Markov decision, Markov decision processes, Constrained Markov, optimizes expected payoffs, sequential decision making
类目: Artificial Intelligence (cs.AI)
*备注:

点击查看摘要

Abstract:Constrained Markov decision processes (CMDPs), in which the agent optimizes expected payoffs while keeping the expected cost below a given threshold, are the leading framework for safe sequential decision making under stochastic uncertainty. Among algorithms for planning and learning in CMDPs, methods based on Monte Carlo tree search (MCTS) have particular importance due to their efficiency and extendibility to more complex frameworks (such as partially observable settings and games). However, current MCTS-based methods for CMDPs either struggle with finding safe (i.e., constraint-satisfying) policies, or are too conservative and do not find valuable policies. We introduce Threshold UCT (T-UCT), an online MCTS-based algorithm for CMDP planning. Unlike previous MCTS-based CMDP planners, T-UCT explicitly estimates Pareto curves of cost-utility trade-offs throughout the search tree, using these together with a novel action selection and threshold update rules to seek safe and valuable policies. Our experiments demonstrate that our approach significantly outperforms state-of-the-art methods from the literature.

[AI-14] Spatio-Temporal Forecasting of PM2.5 via Spatial-Diffusion guided Encoder-Decoder Architecture

链接: https://arxiv.org/abs/2412.13935
作者: Malay Pandey,Vaishali Jain,Nimit Godhani,Sachchida Nand Tripathi,Piyush Rai
关键词: exhibit spatio-temporal correlations, Graph Neural Network, require spatio-temporal forecasting, problem settings, settings that require
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
*备注: 9 pages, 4 figures, International Conference on Data Science and Management of Data (CODS-COMAD), IIT Jodhpur, 2024

点击查看摘要

Abstract:In many problem settings that require spatio-temporal forecasting, the values in the time-series not only exhibit spatio-temporal correlations but are also influenced by spatial diffusion across locations. One such example is forecasting the concentration of fine particulate matter (PM2.5) in the atmosphere which is influenced by many complex factors, the most important ones being diffusion due to meteorological factors as well as transport across vast distances over a period of time. We present a novel Spatio-Temporal Graph Neural Network architecture, that specifically captures these dependencies to forecast the PM2.5 concentration. Our model is based on an encoder-decoder architecture where the encoder and decoder parts leverage gated recurrent units (GRU) augmented with a graph neural network (TransformerConv) to account for spatial diffusion. Our model can also be seen as a generalization of various existing models for time-series or spatio-temporal forecasting. We demonstrate the model’s effectiveness on two real-world PM2.5 datasets: (1) data collected by us using a recently deployed network of low-cost PM _2.5 sensors from 511 locations spanning the entirety of the Indian state of Bihar over a period of one year, and (2) another publicly available dataset that covers severely polluted regions from China for a period of 4 years. Our experimental results show our model’s impressive ability to account for both spatial as well as temporal dependencies precisely.

[AI-15] Energy-Efficient SLAM via Joint Design of Sensing Communication and Exploration Speed

链接: https://arxiv.org/abs/2412.13912
作者: Zidong Han,Ruibo Jin,Xiaoyang Li,Bingpeng Zhou,Qinyu Zhang,Yi Gong
关键词: machine intelligence applications, drawn significant attentions, support future spatial, future spatial machine, spatial machine intelligence
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
*备注:

点击查看摘要

Abstract:To support future spatial machine intelligence applications, lifelong simultaneous localization and mapping (SLAM) has drawn significant attentions. SLAM is usually realized based on various types of mobile robots performing simultaneous and continuous sensing and communication. This paper focuses on analyzing the energy efficiency of robot operation for lifelong SLAM by jointly considering sensing, communication and mechanical factors. The system model is built based on a robot equipped with a 2D light detection and ranging (LiDAR) and an odometry. The cloud point raw data as well as the odometry data are wirelessly transmitted to data center where real-time map reconstruction is realized based on an unsupervised deep learning based method. The sensing duration, transmit power, transmit duration and exploration speed are jointly optimized to minimize the energy consumption. Simulations and experiments demonstrate the performance of our proposed method.

[AI-16] Resource Constrained Pathfinding with Enhanced Bidirectional A* Search AAAI

链接: https://arxiv.org/abs/2412.13888
作者: Saman Ahmadi,Andrea Raith,Guido Tack,Mahdi Jalili
关键词: Resource Constrained Shortest, Constrained Shortest Path, cost optimal path, classic Resource Constrained, Shortest Path
类目: Artificial Intelligence (cs.AI)
*备注: 9 pages, 3 figures, 2 tables, The 39th Annual AAAI Conference on Artificial Intelligence

点击查看摘要

Abstract:The classic Resource Constrained Shortest Path (RCSP) problem aims to find a cost optimal path between a pair of nodes in a network such that the resources used in the path are within a given limit. Having been studied for over a decade, RCSP has seen recent solutions that utilize heuristic-guided search to solve the constrained problem faster. Building upon the bidirectional A* search paradigm, this research introduces a novel constrained search framework that uses efficient pruning strategies to allow for accelerated and effective RCSP search in large-scale networks. Results show that, compared to the state of the art, our enhanced framework can significantly reduce the constrained search time, achieving speed-ups of over to two orders of magnitude.

[AI-17] RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation

链接: https://arxiv.org/abs/2412.13877
作者: Kun Wu,Chengkai Hou,Jiaming Liu,Zhengping Che,Xiaozhu Ju,Zhuqin Yang,Meng Li,Yinuo Zhao,Zhiyuan Xu,Guang Yang,Zhen Zhao,Guangyu Li,Zhao Jin,Lecheng Wang,Jilei Mao,Xinhua Wang,Shichao Fan,Ning Liu,Pei Ren,Qiang Zhang,Yaoxu Lyu,Mengzhen Liu,Jingyang He,Yulin Luo,Zeyu Gao,Chenxuan Li,Chenyang Gu,Yankai Fu,Di Wu,Xingyu Wang,Sixiang Chen,Zhenyu Wang,Pengju An,Siyuan Qian,Shanghang Zhang,Jian Tang
关键词: Developing robust, robust and general-purpose, key goal, general-purpose robotic manipulation, robotic manipulation policies
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
*备注:

点击查看摘要

Abstract:Developing robust and general-purpose robotic manipulation policies is a key goal in the field of robotics. To achieve effective generalization, it is essential to construct comprehensive datasets that encompass a large number of demonstration trajectories and diverse tasks. Unlike vision or language data that can be collected from the Internet, robotic datasets require detailed observations and manipulation actions, necessitating significant investment in hardware-software infrastructure and human labor. While existing works have focused on assembling various individual robot datasets, there remains a lack of a unified data collection standard and insufficient diversity in tasks, scenarios, and robot types. In this paper, we introduce RoboMIND (Multi-embodiment Intelligence Normative Data for Robot manipulation), featuring 55k real-world demonstration trajectories across 279 diverse tasks involving 61 different object classes. RoboMIND is collected through human teleoperation and encompasses comprehensive robotic-related information, including multi-view RGB-D images, proprioceptive robot state information, end effector details, and linguistic task descriptions. To ensure dataset consistency and reliability during policy learning, RoboMIND is built on a unified data collection platform and standardized protocol, covering four distinct robotic embodiments. We provide a thorough quantitative and qualitative analysis of RoboMIND across multiple dimensions, offering detailed insights into the diversity of our datasets. In our experiments, we conduct extensive real-world testing with four state-of-the-art imitation learning methods, demonstrating that training with RoboMIND data results in a high manipulation success rate and strong generalization. Our project is at this https URL.

[AI-18] SHAP scores fail pervasively even when Lipschitz succeeds

链接: https://arxiv.org/abs/2412.13866
作者: Olivier Letoffe,Xuanxiang Huang,Joao Marques-Silva
关键词: SHAP scores, SHAP, computed SHAP scores, paper shows, tool SHAP
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
*备注:

点击查看摘要

Abstract:The ubiquitous use of Shapley values in eXplainable AI (XAI) has been triggered by the tool SHAP, and as a result are commonly referred to as SHAP scores. Recent work devised examples of machine learning (ML) classifiers for which the computed SHAP scores are thoroughly unsatisfactory, by allowing human decision-makers to be misled. Nevertheless, such examples could be perceived as somewhat artificial, since the selected classes must be interpreted as numeric. Furthermore, it was unclear how general were the issues identified with SHAP scores. This paper answers these criticisms. First, the paper shows that for Boolean classifiers there are arbitrarily many examples for which the SHAP scores must be deemed unsatisfactory. Second, the paper shows that the issues with SHAP scores are also observed in the case of regression models. In addition, the paper studies the class of regression models that respect Lipschitz continuity, a measure of a function’s rate of change that finds important recent uses in ML, including model robustness. Concretely, the paper shows that the issues with SHAP scores occur even for regression models that respect Lipschitz continuity. Finally, the paper shows that the same issues are guaranteed to exist for arbitrarily differentiable regression models.

[AI-19] IDEQ: an improved diffusion model for the TSP

链接: https://arxiv.org/abs/2412.13858
作者: Mickael Basson,Philippe Preux
关键词: Traveling Salesman Problem, Salesman Problem, Traveling Salesman, investigate diffusion models, solve the Traveling
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:We investigate diffusion models to solve the Traveling Salesman Problem. Building on the recent DIFUSCO and T2TCO approaches, we propose IDEQ. IDEQ improves the quality of the solutions by leveraging the constrained structure of the state space of the TSP. Another key component of IDEQ consists in replacing the last stages of DIFUSCO curriculum learning by considering a uniform distribution over the Hamiltonian tours whose orbits by the 2-opt operator converge to the optimal solution as the training objective. Our experiments show that IDEQ improves the state of the art for such neural network based techniques on synthetic instances. More importantly, our experiments show that IDEQ performs very well on the instances of the TSPlib, a reference benchmark in the TSP community: it closely matches the performance of the best heuristics, LKH3, being even able to obtain better solutions than LKH3 on 2 instances of the TSPlib defined on 1577 and 3795 cities. IDEQ obtains 0.3% optimality gap on TSP instances made of 500 cities, and 0.5% on TSP instances with 1000 cities. This sets a new SOTA for neural based methods solving the TSP. Moreover, IDEQ exhibits a lower variance and better scales-up with the number of cities with regards to DIFUSCO and T2TCO.

[AI-20] From approximation error to optimality gap - Explaining the performance impact of opportunity cost approximation in integrated demand management and vehicle routing

链接: https://arxiv.org/abs/2412.13851
作者: David Fleckenstein,Robert Klein,Vienna Klein,Claudius Steinhardt
关键词: digital distribution channels, logistical service providers, manage booking processes, booking processes actively, vehicle routing problems
类目: Artificial Intelligence (cs.AI)
*备注:

点击查看摘要

Abstract:The widespread adoption of digital distribution channels both enables and forces more and more logistical service providers to manage booking processes actively to maintain competitiveness. As a result, their operational planning is no longer limited to solving vehicle routing problems. Instead, demand management decisions and vehicle routing decisions are optimized integratively with the aim of maximizing revenue and minimizing fulfillment cost. The resulting integrated demand management and vehicle routing problems (i-DMVRPs) can be formulated as Markov decision process models and, theoretically, can be solved via the well-known Bellman equation. Unfortunately, the Bellman equation is intractable for realistic-sized instances. Thus, in the literature, i-DMVRPs are often addressed via decomposition-based solution approaches involving an opportunity cost approximation as a key component. Despite its importance, to the best of our knowledge, there is neither a technique to systematically analyze how the accuracy of the opportunity cost approximation translates into overall solution quality nor are there general guidelines on when to apply which class of approximation approach. In this work, we address this research gap by proposing an explainability technique that quantifies and visualizes the magnitude of approximation errors, their immediate impact, and their relevance in specific regions of the state space. Exploiting reward decomposition, it further yields a characterization of different types of approximation errors. Applying the technique to a generic i-DMVRP in a full-factorial computational study and comparing the results with observations in existing literature, we show that the technique contributes to better explaining algorithmic performance and provides guidance for the algorithm selection and development process.

[AI-21] A Concept-Centric Approach to Multi-Modality Learning

链接: https://arxiv.org/abs/2412.13847
作者: Yuchong Geng,Ao Tang
关键词: process distinct modality, distinct modality inputs, concept space, modality-agnostic concept space, concept space possessing
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:In an effort to create a more efficient AI system, we introduce a new multi-modality learning framework that leverages a modality-agnostic concept space possessing abstract knowledge and a set of modality-specific projection models tailored to process distinct modality inputs and map them onto the concept space. Decoupled from specific modalities and their associated projection models, the concept space focuses on learning abstract knowledge that is universally applicable across modalities. Subsequently, the knowledge embedded into the concept space streamlines the learning processes of modality-specific projection models. We evaluate our framework on two popular tasks: Image-Text Matching and Visual Question Answering. Our framework achieves performance on par with benchmark models while demonstrating more efficient learning curves.

[AI-22] From Expectation to Habit: Why Do Software Practitioners Adopt Fairness Toolkits?

链接: https://arxiv.org/abs/2412.13846
作者: Gianmario Voria,Stefano Lambiase,Maria Concetta Schiavone,Gemma Catolino,Fabio Palomba
关键词: systems continues, machine learning, grow across industries, center stage, continues to grow
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
*备注:

点击查看摘要

Abstract:As the adoption of machine learning (ML) systems continues to grow across industries, concerns about fairness and bias in these systems have taken center stage. Fairness toolkits, designed to mitigate bias in ML models, serve as critical tools for addressing these ethical concerns. However, their adoption in the context of software development remains underexplored, especially regarding the cognitive and behavioral factors driving their usage. As a deeper understanding of these factors could be pivotal in refining tool designs and promoting broader adoption, this study investigates the factors influencing the adoption of fairness toolkits from an individual perspective. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT2), we examined the factors shaping the intention to adopt and actual use of fairness toolkits. Specifically, we employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze data from a survey study involving practitioners in the software industry. Our findings reveal that performance expectancy and habit are the primary drivers of fairness toolkit adoption. These insights suggest that by emphasizing the effectiveness of these tools in mitigating bias and fostering habitual use, organizations can encourage wider adoption. Practical recommendations include improving toolkit usability, integrating bias mitigation processes into routine development workflows, and providing ongoing support to ensure professionals see clear benefits from regular use.

[AI-23] CRM: Retrieval Model with Controllable Condition

链接: https://arxiv.org/abs/2412.13844
作者: Chi Liu,Jiangxia Cao,Rui Huang,Kuo Cai,Weifeng Ding,Qiang Luo,Kun Gai,Guorui Zhou
关键词: item candidates satisfied, retrieval model, retrieval, Controllable Retrieval Model, item candidates
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
*备注:

点击查看摘要

Abstract:Recommendation systems (RecSys) are designed to connect users with relevant items from a vast pool of candidates while aligning with the business goals of the platform. A typical industrial RecSys is composed of two main stages, retrieval and ranking: (1) the retrieval stage aims at searching hundreds of item candidates satisfied user interests; (2) based on the retrieved items, the ranking stage aims at selecting the best dozen items by multiple targets estimation for each item candidate, including classification and regression targets. Compared with ranking model, the retrieval model absence of item candidate information during inference, therefore retrieval models are often trained by classification target only (e.g., click-through rate), but failed to incorporate regression target (e.g., the expected watch-time), which limit the effectiveness of retrieval. In this paper, we propose the Controllable Retrieval Model (CRM), which integrates regression information as conditional features into the two-tower retrieval paradigm. This modification enables the retrieval stage could fulfill the target gap with ranking model, enhancing the retrieval model ability to search item candidates satisfied the user interests and condition effectively. We validate the effectiveness of CRM through real-world A/B testing and demonstrate its successful deployment in Kuaishou short-video recommendation system, which serves over 400 million users.

[AI-24] AI Perceptions Across Cultures: Similarities and Differences in Expectations Risks Benefits Tradeoffs and Value in Germany and China

链接: https://arxiv.org/abs/2412.13841
作者: Philipp Brauner,Felix Glawe,Gian Luca Liehner,Luisa Vervier,Martina Ziefle
关键词: guiding research priorities, shaping public discourse, continues to advance, including biases, understanding public perceptions
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
*备注:

点击查看摘要

Abstract:As artificial intelligence (AI) continues to advance, understanding public perceptions – including biases, risks, and benefits – is critical for guiding research priorities, shaping public discourse, and informing policy. This study explores public mental models of AI using micro scenarios to assess reactions to 71 statements about AI’s potential future impacts. Drawing on cross-cultural samples from Germany (N=52) and China (N=60), we identify significant differences in expectations, evaluations, and risk-utility tradeoffs. German participants tended toward more cautious assessments, whereas Chinese participants expressed greater optimism regarding AI’s societal benefits. Chinese participants exhibited relatively balanced risk-benefit tradeoffs ( \beta=-0.463 for risk and \beta=+0.484 for benefit, r^2=.630 ). In contrast, German participants showed a stronger emphasis on AI benefits and less on risks ( \beta=-0.337 for risk and \beta=+0.715 for benefit, r^2=.839 ). Visual cognitive maps illustrate these contrasts, offering new perspectives on how cultural contexts shape AI acceptance. Our findings underline key factors influencing public perception and provide actionable insights for fostering equitable and culturally sensitive integration of AI technologies.

[AI-25] Maybe you are looking for CroQS: Cross-modal Query Suggestion for Text-to-Image Retrieval ECIR

链接: https://arxiv.org/abs/2412.13834
作者: Giacomo Pacini,Fabio Carrara,Nicola Messina,Nicola Tonellotto,Giuseppe Amato,Fabrizio Falchi
关键词: enhances system interactivity, technique widely adopted, Query suggestion, query suggestion solutions, explored query suggestion
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
*备注: 15 pages, 5 figures. To be published as full paper in the Proceedings of the European Conference on Information Retrieval (ECIR) 2025

点击查看摘要

Abstract:Query suggestion, a technique widely adopted in information retrieval, enhances system interactivity and the browsing experience of document collections. In cross-modal retrieval, many works have focused on retrieving relevant items from natural language queries, while few have explored query suggestion solutions. In this work, we address query suggestion in cross-modal retrieval, introducing a novel task that focuses on suggesting minimal textual modifications needed to explore visually consistent subsets of the collection, following the premise of ‘‘Maybe you are looking for’’. To facilitate the evaluation and development of methods, we present a tailored benchmark named CroQS. This dataset comprises initial queries, grouped result sets, and human-defined suggested queries for each group. We establish dedicated metrics to rigorously evaluate the performance of various methods on this task, measuring representativeness, cluster specificity, and similarity of the suggested queries to the original ones. Baseline methods from related fields, such as image captioning and content summarization, are adapted for this task to provide reference performance scores. Although relatively far from human performance, our experiments reveal that both LLM-based and captioning-based methods achieve competitive results on CroQS, improving the recall on cluster specificity by more than 115% and representativeness mAP by more than 52% with respect to the initial query. The dataset, the implementation of the baseline methods and the notebooks containing our experiments are available here: this https URL

[AI-26] Heterogeneous Graph Collaborative Filtering WSDM’2025

链接: https://arxiv.org/abs/2412.13825
作者: Lianghao Xia,Meiyan Xie,Yong Xu,Chao Huang
关键词: modern recommender systems, low-dimensional latent representations, recommender systems, modern recommender, representations to embed
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
*备注: This paper is accepted by WSDM’2025

点击查看摘要

Abstract:For modern recommender systems, the use of low-dimensional latent representations to embed users and items based on their observed interactions has become commonplace. However, many existing recommendation models are primarily designed for coarse-grained and homogeneous interactions, which limits their effectiveness in two critical dimensions. Firstly, these models fail to leverage the relational dependencies that exist across different types of user behaviors, such as page views, collects, comments, and purchases. Secondly, they struggle to capture the fine-grained latent factors that drive user interaction patterns. To address these limitations, we present a heterogeneous graph collaborative filtering model MixRec that excels at disentangling users’ multi-behavior interaction patterns and uncovering the latent intent factors behind each behavior. Our model achieves this by incorporating intent disentanglement and multi-behavior modeling, facilitated by a parameterized heterogeneous hypergraph architecture. Furthermore, we introduce a novel contrastive learning paradigm that adaptively explores the advantages of self-supervised data augmentation, thereby enhancing the model’s resilience against data sparsity and expressiveness with relation heterogeneity. To validate the efficacy of MixRec, we conducted extensive experiments on three public datasets. The results clearly demonstrate its superior performance, significantly outperforming various state-of-the-art baselines. Our model is open-sourced and available at: this https URL.

[AI-27] Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN

链接: https://arxiv.org/abs/2412.13795
作者: Pengxiang Li,Lu Yin,Shiwei Liu
关键词: Large Language Models, Large Language, achieved remarkable success, recent findings reveal, Language Models
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
*备注:

点击查看摘要

Abstract:Large Language Models (LLMs) have achieved remarkable success, yet recent findings reveal that their deeper layers often contribute minimally and can be pruned without affecting overall performance. While some view this as an opportunity for model compression, we identify it as a training shortfall rooted in the widespread use of Pre-Layer Normalization (Pre-LN). We demonstrate that Pre-LN, commonly employed in models like GPT and LLaMA, leads to diminished gradient norms in its deeper layers, reducing their effectiveness. In contrast, Post-Layer Normalization (Post-LN) preserves larger gradient norms in deeper layers but suffers from vanishing gradients in earlier layers. To address this, we introduce Mix-LN, a novel normalization technique that combines the strengths of Pre-LN and Post-LN within the same model. Mix-LN applies Post-LN to the earlier layers and Pre-LN to the deeper layers, ensuring more uniform gradients across layers. This allows all parts of the network–both shallow and deep layers–to contribute effectively to training. Extensive experiments with various model sizes from 70M to 7B demonstrate that Mix-LN consistently outperforms both Pre-LN and Post-LN, promoting more balanced, healthier gradient norms throughout the network, and enhancing the overall quality of LLM pre-training. Furthermore, we demonstrate that models pre-trained with Mix-LN learn better compared to those using Pre-LN or Post-LN during supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), highlighting the critical importance of high-quality deep layers. By effectively addressing the inefficiencies of deep layers in current LLMs, Mix-LN unlocks their potential, enhancing model capacity without increasing model size. Our code is available at this https URL.

[AI-28] Uncertainty separation via ensemble quantile regression

链接: https://arxiv.org/abs/2412.13738
作者: Navid Ansari,Hans-Peter Seidel,Vahid Babaei
关键词: data driven modeling, reliable uncertainty quantification, paper introduces, data driven, driven modeling
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
*备注:

点击查看摘要

Abstract:This paper introduces a novel and scalable framework for uncertainty estimation and separation with applications in data driven modeling in science and engineering tasks where reliable uncertainty quantification is critical. Leveraging an ensemble of quantile regression (E-QR) models, our approach enhances aleatoric uncertainty estimation while preserving the quality of epistemic uncertainty, surpassing competing methods, such as Deep Ensembles (DE) and Monte Carlo (MC) dropout. To address challenges in separating uncertainty types, we propose an algorithm that iteratively improves separation through progressive sampling in regions of high uncertainty. Our framework is scalable to large datasets and demonstrates superior performance on synthetic benchmarks, offering a robust tool for uncertainty quantification in data-driven applications.

[AI-29] On the Compression of Language Models for Code: An Empirical Study on CodeBERT

链接: https://arxiv.org/abs/2412.13737
作者: Giordano d’Aloisio,Luca Traini,Federica Sarro,Antinisca Di Marco
关键词: Language models, practical adoption, proven successful, wide range, hinder their practical
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Performance (cs.PF)
*备注:

点击查看摘要

Abstract:Language models have proven successful across a wide range of software engineering tasks, but their significant computational costs often hinder their practical adoption. To address this challenge, researchers have begun applying various compression strategies to improve the efficiency of language models for code. These strategies aim to optimize inference latency and memory usage, though often at the cost of reduced model effectiveness. However, there is still a significant gap in understanding how these strategies influence the efficiency and effectiveness of language models for code. Here, we empirically investigate the impact of three well-known compression strategies – knowledge distillation, quantization, and pruning – across three different classes of software engineering tasks: vulnerability detection, code summarization, and code search. Our findings reveal that the impact of these strategies varies greatly depending on the task and the specific compression method employed. Practitioners and researchers can use these insights to make informed decisions when selecting the most appropriate compression strategy, balancing both efficiency and effectiveness based on their specific needs.

[AI-30] An Algebraic Notion of Conditional Independence and Its Application to Knowledge Representation (full version) AAAI2025

链接: https://arxiv.org/abs/2412.13712
作者: Jesse Heyninck
关键词: crucial concept supporting, concept supporting adequate, supporting adequate modelling, Conditional independence, Conditional
类目: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
*备注: Full version, including proofs, of paper accepted at AAAI 2025

点击查看摘要

Abstract:Conditional independence is a crucial concept supporting adequate modelling and efficient reasoning in probabilistics. In knowledge representation, the idea of conditional independence has also been introduced for specific formalisms, such as propositional logic and belief revision. In this paper, the notion of conditional independence is studied in the algebraic framework of approximation fixpoint theory. This gives a language-independent account of conditional independence that can be straightforwardly applied to any logic with fixpoint semantics. It is shown how this notion allows to reduce global reasoning to parallel instances of local reasoning, leading to fixed-parameter tractability results. Furthermore, relations to existing notions of conditional independence are discussed and the framework is applied to normal logic programming.

[AI-31] Exploring Multi-Modal Integration with Tool-Augmented LLM Agents for Precise Causal Discovery

链接: https://arxiv.org/abs/2412.13667
作者: ChengAo Shen,Zhengzhang Chen,Dongsheng Luo,Dongkuan Xu,Haifeng Chen,Jingchao Ni
关键词: causal discovery, Large Language Models, decision-making across domains, smart health, imperative foundation
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Methodology (stat.ME)
*备注:

点击查看摘要

Abstract:Causal inference is an imperative foundation for decision-making across domains, such as smart health, AI for drug discovery and AIOps. Traditional statistical causal discovery methods, while well-established, predominantly rely on observational data and often overlook the semantic cues inherent in cause-and-effect relationships. The advent of Large Language Models (LLMs) has ushered in an affordable way of leveraging the semantic cues for knowledge-driven causal discovery, but the development of LLMs for causal discovery lags behind other areas, particularly in the exploration of multi-modality data. To bridge the gap, we introduce MATMCD, a multi-agent system powered by tool-augmented LLMs. MATMCD has two key agents: a Data Augmentation agent that retrieves and processes modality-augmented data, and a Causal Constraint agent that integrates multi-modal data for knowledge-driven inference. Delicate design of the inner-workings ensures successful cooperation of the agents. Our empirical study across seven datasets suggests the significant potential of multi-modality enhanced causal discovery.

[AI-32] An Extension-Based Argument-Ranking Semantics: Social Rankings in Abstract Argumentation Long Version

链接: https://arxiv.org/abs/2412.13632
作者: Lars Bengel,Giovanni Buraglio,Jan Maly,Kenneth Skiba
关键词: credulously accepted, classification of arguments, arguments into skeptically, skeptically accepted, accepted and rejected
类目: Artificial Intelligence (cs.AI)
*备注:

点击查看摘要

Abstract:In this paper, we introduce a new family of argument-ranking semantics which can be seen as a refinement of the classification of arguments into skeptically accepted, credulously accepted and rejected. To this end we use so-called social ranking functions which have been developed recently to rank individuals based on their performance in groups. We provide necessary and sufficient conditions for a social ranking function to give rise to an argument-ranking semantics satisfying the desired refinement property.

[AI-33] Policy Decorator: Model-Agnostic Online Refinement for Large Policy Model

链接: https://arxiv.org/abs/2412.13630
作者: Xiu Yuan,Tongzhou Mu,Stone Tao,Yunhao Fang,Mengke Zhang,Hao Su
关键词: Recent advancements, imitation learning models, Policy Decorator, imitation learning, develop effective policies
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
*备注: Explore videos, data, code, and more at this https URL

点击查看摘要

Abstract:Recent advancements in robot learning have used imitation learning with large models and extensive demonstrations to develop effective policies. However, these models are often limited by the quantity, quality, and diversity of demonstrations. This paper explores improving offline-trained imitation learning models through online interactions with the environment. We introduce Policy Decorator, which uses a model-agnostic residual policy to refine large imitation learning models during online interactions. By implementing controlled exploration strategies, Policy Decorator enables stable, sample-efficient online learning. Our evaluation spans eight tasks across two benchmarks-ManiSkill and Adroit-and involves two state-of-the-art imitation learning models (Behavior Transformer and Diffusion Policy). The results show Policy Decorator effectively improves the offline-trained policies and preserves the smooth motion of imitation learning models, avoiding the erratic behaviors of pure RL policies. See our project page (this https URL) for videos.

[AI-34] Unifying Attribution-Based Explanations Using Functional Decomposition

链接: https://arxiv.org/abs/2412.13623
作者: Arne Gevaert,Yvan Saeys
关键词: black box problem, explanation methods, attribution method, complex models, method
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
*备注:

点击查看摘要

Abstract:The black box problem in machine learning has led to the introduction of an ever-increasing set of explanation methods for complex models. These explanations have different properties, which in turn has led to the problem of method selection: which explanation method is most suitable for a given use case? In this work, we propose a unifying framework of attribution-based explanation methods, which provides a step towards a rigorous study of the similarities and differences of explanations. We first introduce removal-based attribution methods (RBAMs), and show that an extensively broad selection of existing methods can be viewed as such RBAMs. We then introduce the canonical additive decomposition (CAD). This is a general construction for additively decomposing any function based on the central idea of removing (groups of) features. We proceed to show that indeed every valid additive decomposition is an instance of the CAD, and that any removal-based attribution method is associated with a specific CAD. Next, we show that any removal-based attribution method can be completely defined as a game-theoretic value or interaction index for a specific (possibly constant-shifted) cooperative game, which is defined using the corresponding CAD of the method. We then use this intrinsic connection to define formal descriptions of specific behaviours of explanation methods, which we also call functional axioms, and identify sufficient conditions on the corresponding CAD and game-theoretic value or interaction index of an attribution method under which the attribution method is guaranteed to adhere to these functional axioms. Finally, we show how this unifying framework can be used to develop new, efficient approximations for existing explanation methods.

[AI-35] NPC: Neural Predictive Control for Fuel-Efficient Autonomous Trucks WWW ATC

链接: https://arxiv.org/abs/2412.13618
作者: Jiaping Ren,Jiahao Xiang,Hongfei Gao,Jinchuan Zhang,Yiming Ren,Yuexin Ma,Yi Wu,Ruigang Yang,Wei Li
关键词: decrease carbon emissions, long-distance cargo transportation, Brake-specific Fuel Consumption, Neural Predictive Control, Fuel Consumption
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
*备注: 7 pages, 6 figures, for associated mpeg file, see this https URL

点击查看摘要

Abstract:Fuel efficiency is a crucial aspect of long-distance cargo transportation by oil-powered trucks that economize on costs and decrease carbon emissions. Current predictive control methods depend on an accurate model of vehicle dynamics and engine, including weight, drag coefficient, and the Brake-specific Fuel Consumption (BSFC) map of the engine. We propose a pure data-driven method, Neural Predictive Control (NPC), which does not use any physical model for the vehicle. After training with over 20,000 km of historical data, the novel proposed NVFormer implicitly models the relationship between vehicle dynamics, road slope, fuel consumption, and control commands using the attention mechanism. Based on the online sampled primitives from the past of the current freight trip and anchor-based future data synthesis, the NVFormer can infer optimal control command for reasonable fuel consumption. The physical model-free NPC outperforms the base PCC method with 2.41% and 3.45% more significant fuel saving in simulation and open-road highway testing, respectively.

[AI-36] Exploiting Symmetries in MUS Computation (Extended version) AAAI25

链接: https://arxiv.org/abs/2412.13606
作者: Ignace Bleukx,Hélène Verhaeghe,Bart Bogaerts,Tias Guns
关键词: Minimal Unsatisfiable Subset, extract a Minimal, Unsatisfiable Subset, Minimal Unsatisfiable, eXplainable Constraint Solving
类目: Artificial Intelligence (cs.AI)
*备注: Accepted at AAAI25 conference

点击查看摘要

Abstract:In eXplainable Constraint Solving (XCS), it is common to extract a Minimal Unsatisfiable Subset (MUS) from a set of unsatisfiable constraints. This helps explain to a user why a constraint specification does not admit a solution. Finding MUSes can be computationally expensive for highly symmetric problems, as many combinations of constraints need to be considered. In the traditional context of solving satisfaction problems, symmetry has been well studied, and effective ways to detect and exploit symmetries during the search exist. However, in the setting of finding MUSes of unsatisfiable constraint programs, symmetries are understudied. In this paper, we take inspiration from existing symmetry-handling techniques and adapt well-known MUS-computation methods to exploit symmetries in the specification, speeding-up overall computation time. Our results display a significant reduction of runtime for our adapted algorithms compared to the baseline on symmetric problems.

[AI-37] SemiDFL: A Semi-Supervised Paradigm for Decentralized Federated Learning AAAI2025

链接: https://arxiv.org/abs/2412.13589
作者: Xinyang Liu,Pengchao Han,Xuan Li,Bo Liu
关键词: Decentralized federated learning, Decentralized federated, mitigating communication bottlenecks, single-point failure issue, failure issue present
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注: Accepted by AAAI 2025

点击查看摘要

Abstract:Decentralized federated learning (DFL) realizes cooperative model training among connected clients without relying on a central server, thereby mitigating communication bottlenecks and eliminating the single-point failure issue present in centralized federated learning (CFL). Most existing work on DFL focuses on supervised learning, assuming each client possesses sufficient labeled data for local training. However, in real-world applications, much of the data is unlabeled. We address this by considering a challenging yet practical semisupervised learning (SSL) scenario in DFL, where clients may have varying data sources: some with few labeled samples, some with purely unlabeled data, and others with both. In this work, we propose SemiDFL, the first semi-supervised DFL method that enhances DFL performance in SSL scenarios by establishing a consensus in both data and model spaces. Specifically, we utilize neighborhood information to improve the quality of pseudo-labeling, which is crucial for effectively leveraging unlabeled data. We then design a consensusbased diffusion model to generate synthesized data, which is used in combination with pseudo-labeled data to create mixed datasets. Additionally, we develop an adaptive aggregation method that leverages the model accuracy of synthesized data to further enhance SemiDFL performance. Through extensive experimentation, we demonstrate the remarkable performance superiority of the proposed DFL-Semi method over existing CFL and DFL schemes in both IID and non-IID SSL scenarios.

[AI-38] Bridging the User-side Knowledge Gap in Knowledge-aware Recommendations with Large Language Models AAAI2025

链接: https://arxiv.org/abs/2412.13544
作者: Zheng Hu,Zhe Li,Ziyun Jiao,Satoshi Nakagawa,Jiawen Deng,Shimin Cai,Tao Zhou,Fuji Ren
关键词: enhancing recommendation accuracy, Large Language Models, knowledge, recent years, Language Models
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
*备注: Accepted at AAAI 2025

点击查看摘要

Abstract:In recent years, knowledge graphs have been integrated into recommender systems as item-side auxiliary information, enhancing recommendation accuracy. However, constructing and integrating structural user-side knowledge remains a significant challenge due to the improper granularity and inherent scarcity of user-side features. Recent advancements in Large Language Models (LLMs) offer the potential to bridge this gap by leveraging their human behavior understanding and extensive real-world knowledge. Nevertheless, integrating LLM-generated information into recommender systems presents challenges, including the risk of noisy information and the need for additional knowledge transfer. In this paper, we propose an LLM-based user-side knowledge inference method alongside a carefully designed recommendation framework to address these challenges. Our approach employs LLMs to infer user interests based on historical behaviors, integrating this user-side information with item-side and collaborative data to construct a hybrid structure: the Collaborative Interest Knowledge Graph (CIKG). Furthermore, we propose a CIKG-based recommendation framework that includes a user interest reconstruction module and a cross-domain contrastive learning module to mitigate potential noise and facilitate knowledge transfer. We conduct extensive experiments on three real-world datasets to validate the effectiveness of our method. Our approach achieves state-of-the-art performance compared to competitive baselines, particularly for users with sparse interactions.

[AI-39] ROMAS: A Role-Based Multi-Agent System for Database monitoring and Planning

链接: https://arxiv.org/abs/2412.13520
作者: Yi Huang,Fangyin Cheng,Fan Zhou,Jiahui Li,Jian Gong,Hongjun Yang,Zhidong Fan,Caigao Jiang,Siqiao Xue,Faqiang Chen
关键词: Large Language Models, Large Language, Language Models, demonstrated remarkable capabilities, recent years
类目: Artificial Intelligence (cs.AI); Databases (cs.DB); Multiagent Systems (cs.MA)
*备注:

点击查看摘要

Abstract:In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities in data analytics when integrated with Multi-Agent Systems (MAS). However, these systems often struggle with complex tasks that involve diverse functional requirements and intricate data processing challenges, necessitating customized solutions that lack broad applicability. Furthermore, current MAS fail to emulate essential human-like traits such as self-planning, self-monitoring, and collaborative work in dynamic environments, leading to inefficiencies and resource wastage. To address these limitations, we propose ROMAS, a novel Role-Based M ulti-A gent System designed to adapt to various scenarios while enabling low code development and one-click deployment. ROMAS has been effectively deployed in DB-GPT [Xue et al., 2023a, 2024b], a well-known project utilizing LLM-powered database analytics, showcasing its practical utility in real-world scenarios. By integrating role-based collaborative mechanisms for self-monitoring and self-planning, and leveraging existing MAS capabilities to enhance database interactions, ROMAS offers a more effective and versatile solution. Experimental evaluations of ROMAS demonstrate its superiority across multiple scenarios, highlighting its potential to advance the field of multi-agent data analytics.

[AI-40] uning Music Education: AI-Powered Personalization in Learning Music NEURIPS2024

链接: https://arxiv.org/abs/2412.13514
作者: Mayank Sanganeria,Rohan Gala
关键词: AI-driven step-function advances, Recent AI-driven step-function, music education tools, music education, high-quality music education
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)
*备注: 38th Conference on Neural Information Processing Systems (NeurIPS 2024) Creative AI Track

点击查看摘要

Abstract:Recent AI-driven step-function advances in several longstanding problems in music technology are opening up new avenues to create the next generation of music education tools. Creating personalized, engaging, and effective learning experiences are continuously evolving challenges in music education. Here we present two case studies using such advances in music technology to address these challenges. In our first case study we showcase an application that uses Automatic Chord Recognition to generate personalized exercises from audio tracks, connecting traditional ear training with real-world musical contexts. In the second case study we prototype adaptive piano method books that use Automatic Music Transcription to generate exercises at different skill levels while retaining a close connection to musical interests. These applications demonstrate how recent AI developments can democratize access to high-quality music education and promote rich interaction with music in the age of generative AI. We hope this work inspires other efforts in the community, aimed at removing barriers to access to high-quality music education and fostering human participation in musical expression.

[AI-41] GUI Agents : A Survey

链接: https://arxiv.org/abs/2412.13501
作者: Dang Nguyen,Jian Chen,Yu Wang,Gang Wu,Namyong Park,Zhengmian Hu,Hanjia Lyu,Junda Wu,Ryan Aponte,Yu Xia,Xintong Li,Jing Shi,Hongjie Chen,Viet Dac Lai,Zhouhang Xie,Sungchul Kim,Ruiyi Zhang,Tong Yu,Mehrab Tanjim,Nesreen K. Ahmed,Puneet Mathur,Seunghyun Yoon,Lina Yao,Branislav Kveton,Thien Huu Nguyen,Trung Bui,Tianyi Zhou,Ryan A. Rossi,Franck Dernoncourt
关键词: Graphical User Interface, Large Foundation Models, automating human-computer interaction, Graphical User, User Interface
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
*备注:

点击查看摘要

Abstract:Graphical User Interface (GUI) agents, powered by Large Foundation Models, have emerged as a transformative approach to automating human-computer interaction. These agents autonomously interact with digital systems or software applications via GUIs, emulating human actions such as clicking, typing, and navigating visual elements across diverse platforms. Motivated by the growing interest and fundamental importance of GUI agents, we provide a comprehensive survey that categorizes their benchmarks, evaluation metrics, architectures, and training methods. We propose a unified framework that delineates their perception, reasoning, planning, and acting capabilities. Furthermore, we identify important open challenges and discuss key future directions. Finally, this work serves as a basis for practitioners and researchers to gain an intuitive understanding of current progress, techniques, benchmarks, and critical open problems that remain to be addressed.

[AI-42] Federated t-SNE and UMAP for Distributed Data Visualization AAAI2025

链接: https://arxiv.org/abs/2412.13495
作者: Dong Qiao,Xinxian Ma,Jicong Fan
关键词: t-SNE and UMAP, High-dimensional data visualization, big data era, data, science and engineering
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
*备注: The paper was accepted by AAAI 2025

点击查看摘要

Abstract:High-dimensional data visualization is crucial in the big data era and these techniques such as t-SNE and UMAP have been widely used in science and engineering. Big data, however, is often distributed across multiple data centers and subject to security and privacy concerns, which leads to difficulties for the standard algorithms of t-SNE and UMAP. To tackle the challenge, this work proposes Fed-tSNE and Fed-UMAP, which provide high-dimensional data visualization under the framework of federated learning, without exchanging data across clients or sending data to the central server. The main idea of Fed-tSNE and Fed-UMAP is implicitly learning the distribution information of data in a manner of federated learning and then estimating the global distance matrix for t-SNE and UMAP. To further enhance the protection of data privacy, we propose Fed-tSNE+ and Fed-UMAP+. We also extend our idea to federated spectral clustering, yielding algorithms of clustering distributed data. In addition to these new algorithms, we offer theoretical guarantees of optimization convergence, distance and similarity estimation, and differential privacy. Experiments on multiple datasets demonstrate that, compared to the original algorithms, the accuracy drops of our federated algorithms are tiny.

[AI-43] Analysis of Higher-Order Ising Hamiltonians

链接: https://arxiv.org/abs/2412.13489
作者: Yunuo Cen,Zhiwei Zhang,Zixuan Wang,Yimin Wang,Xuanyao Fong
关键词: industrial-level problems due, scale Ising machines, higher-order Ising, hardware limitations, Ising
类目: Artificial Intelligence (cs.AI); Statistical Mechanics (cond-mat.stat-mech); Computational Physics (physics.comp-ph); Quantum Physics (quant-ph)
*备注:

点击查看摘要

Abstract:It is challenging to scale Ising machines for industrial-level problems due to algorithm or hardware limitations. Although higher-order Ising models provide a more compact encoding, they are, however, hard to physically implement. This work proposes a theoretical framework of a higher-order Ising simulator, IsingSim. The Ising spins and gradients in IsingSim are decoupled and self-customizable. We significantly accelerate the simulation speed via a bidirectional approach for differentiating the hyperedge functions. Our proof-of-concept implementation verifies the theoretical framework by simulating the Ising spins with exact and approximate gradients. Experiment results show that our novel framework can be a useful tool for providing design guidelines for higher-order Ising machines.

[AI-44] oward an Insider Threat Education Platform: A Theoretical Literature Review

链接: https://arxiv.org/abs/2412.13446
作者: Haywood Gelman,John D. Hastings,David Kenley,Eleanor Loiacono
关键词: Insider threats, damage systems, organizations are small, small in number, disproportionate ability
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); Social and Information Networks (cs.SI)
*备注: 6 pages

点击查看摘要

Abstract:Insider threats (InTs) within organizations are small in number but have a disproportionate ability to damage systems, information, and infrastructure. Existing InT research studies the problem from psychological, technical, and educational perspectives. Proposed theories include research on psychological indicators, machine learning, user behavioral log analysis, and educational methods to teach employees recognition and mitigation techniques. Because InTs are a human problem, training methods that address InT detection from a behavioral perspective are critical. While numerous technological and psychological theories exist on detection, prevention, and mitigation, few training methods prioritize psychological indicators. This literature review studied peer-reviewed, InT research organized by subtopic and extracted critical theories from psychological, technical, and educational disciplines. In doing so, this is the first study to comprehensively organize research across all three approaches in a manner which properly informs the development of an InT education platform.

[AI-45] Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition

链接: https://arxiv.org/abs/2412.13442
作者: Ruyue Liu,Rong Yin,Xiangzhen Bo,Xiaoshuai Hao,Xingrui Zhou,Yong Liu,Can Ma,Weiping Wang
关键词: gained significant attention, graph data locally, Federated graph learning, graph data, centralized server
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注:

点击查看摘要

Abstract:Federated graph learning (FGL) has gained significant attention for enabling heterogeneous clients to process their private graph data locally while interacting with a centralized server, thus maintaining privacy. However, graph data on clients are typically non-IID, posing a challenge for a single model to perform well across all clients. Another major bottleneck of FGL is the high cost of communication. To address these challenges, we propose a communication-efficient personalized federated graph learning algorithm, CEFGL. Our method decomposes the model parameters into low-rank generic and sparse private models. We employ a dual-channel encoder to learn sparse local knowledge in a personalized manner and low-rank global knowledge in a shared manner. Additionally, we perform multiple local stochastic gradient descent iterations between communication phases and integrate efficient compression techniques into the algorithm. The advantage of CEFGL lies in its ability to capture common and individual knowledge more precisely. By utilizing low-rank and sparse parameters along with compression techniques, CEFGL significantly reduces communication complexity. Extensive experiments demonstrate that our method achieves optimal classification accuracy in a variety of heterogeneous environments across sixteen datasets. Specifically, compared to the state-of-the-art method FedStar, the proposed method (with GIN as the base model) improves accuracy by 5.64% on cross-datasets setting CHEM, reduces communication bits by a factor of 18.58, and reduces the communication time by a factor of 1.65.

[AI-46] Deploying Foundation Model Powered Agent Services: A Survey

链接: https://arxiv.org/abs/2412.13437
作者: Wenchao Xu,Jinyu Chen,Peirong Zheng,Xiaoquan Yi,Tianyi Tian,Wenhui Zhu,Quan Wan,Haozhao Wang,Yunfeng Fan,Qinliang Su,Xuemin Shen
关键词: Artificial General Intelligence, General Intelligence, Artificial General, advancing toward Artificial, powered agent services
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
*备注:

点击查看摘要

Abstract:Foundation model (FM) powered agent services are regarded as a promising solution to develop intelligent and personalized applications for advancing toward Artificial General Intelligence (AGI). To achieve high reliability and scalability in deploying these agent services, it is essential to collaboratively optimize computational and communication resources, thereby ensuring effective resource allocation and seamless service delivery. In pursuit of this vision, this paper proposes a unified framework aimed at providing a comprehensive survey on deploying FM-based agent services across heterogeneous devices, with the emphasis on the integration of model and resource optimization to establish a robust infrastructure for these services. Particularly, this paper begins with exploring various low-level optimization strategies during inference and studies approaches that enhance system scalability, such as parallelism techniques and resource scaling methods. The paper then discusses several prominent FMs and investigates research efforts focused on inference acceleration, including techniques such as model compression and token reduction. Moreover, the paper also investigates critical components for constructing agent services and highlights notable intelligent applications. Finally, the paper presents potential research directions for developing real-time agent services with high Quality of Service (QoS).

[AI-47] Large Language Model Enhanced Recommender Systems: Taxonomy Trend Application and Future

链接: https://arxiv.org/abs/2412.13432
作者: Qidong Liu,Xiangyu Zhao,Yuhao Wang,Yejing Wang,Zijian Zhang,Yuqi Sun,Xiang Li,Maolin Wang,Pengyue Jia,Chong Chen,Wei Huang,Feng Tian
关键词: Large Language Model, Large Language, including recommender systems, Language Model, LLM
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
*备注:

点击查看摘要

Abstract:Large Language Model (LLM) has transformative potential in various domains, including recommender systems (RS). There have been a handful of research that focuses on empowering the RS by LLM. However, previous efforts mainly focus on LLM as RS, which may face the challenge of intolerant inference costs by LLM. Recently, the integration of LLM into RS, known as LLM-Enhanced Recommender Systems (LLMERS), has garnered significant interest due to its potential to address latency and memory constraints in real-world applications. This paper presents a comprehensive survey of the latest research efforts aimed at leveraging LLM to enhance RS capabilities. We identify a critical shift in the field with the move towards incorporating LLM into the online system, notably by avoiding their use during inference. Our survey categorizes the existing LLMERS approaches into three primary types based on the component of the RS model being augmented: Knowledge Enhancement, Interaction Enhancement, and Model Enhancement. We provide an in-depth analysis of each category, discussing the methodologies, challenges, and contributions of recent studies. Furthermore, we highlight several promising research directions that could further advance the field of LLMERS.

[AI-48] Safeguarding System Prompts for LLM s

链接: https://arxiv.org/abs/2412.13426
作者: Zhifeng Jiang,Zhihua Jin,Guoliang He
关键词: Large language models, Large language, guide model outputs, play a crucial, crucial role
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
*备注: 20 pages, 7 figures, 6 tables

点击查看摘要

Abstract:Large language models (LLMs) are increasingly utilized in applications where system prompts, which guide model outputs, play a crucial role. These prompts often contain business logic and sensitive information, making their protection essential. However, adversarial and even regular user queries can exploit LLM vulnerabilities to expose these hidden prompts. To address this issue, we present PromptKeeper, a novel defense mechanism for system prompt privacy. By reliably detecting worst-case leakage and regenerating outputs without the system prompt when necessary, PromptKeeper ensures robust protection against prompt extraction attacks via either adversarial or regular queries, while preserving conversational capability and runtime efficiency during benign user interactions.

[AI-49] Generating Diverse Hypotheses for Inductive Reasoning

链接: https://arxiv.org/abs/2412.13422
作者: Kang-il Lee,Hyukhun Koh,Dongryeol Lee,Seunghyun Yoon,Minsung Kim,Kyomin Jung
关键词: inferring general rules, Inductive reasoning, process of inferring, inferring general, small number
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
*备注: 14 pages

点击查看摘要

Abstract:Inductive reasoning - the process of inferring general rules from a small number of observations - is a fundamental aspect of human intelligence. Recent works suggest that large language models (LLMs) can engage in inductive reasoning by sampling multiple hypotheses about the rules and selecting the one that best explains the observations. However, due to the IID sampling, semantically redundant hypotheses are frequently generated, leading to significant wastage of compute. In this paper, we 1) demonstrate that increasing the temperature to enhance the diversity is limited due to text degeneration issue, and 2) propose a novel method to improve the diversity while maintaining text quality. We first analyze the effect of increasing the temperature parameter, which is regarded as the LLM’s diversity control, on IID hypotheses. Our analysis shows that as temperature rises, diversity and accuracy of hypotheses increase up to a certain point, but this trend saturates due to text degeneration. To generate hypotheses that are more semantically diverse and of higher quality, we propose a novel approach inspired by human inductive reasoning, which we call Mixture of Concepts (MoC). When applied to several inductive reasoning benchmarks, MoC demonstrated significant performance improvements compared to standard IID sampling and other approaches.

[AI-50] Lightweight yet Fine-grained: A Graph Capsule Convolutional Network with Subspace Alignment for Shared-account Sequential Recommendation AAAI-2025

链接: https://arxiv.org/abs/2412.13408
作者: Jinyu Zhang,Zhongying Zhao,Chao Li,Yanwei Yu
关键词: Shared-account Sequential Recommendation, provide personalized recommendations, Graph Capsule Convolutional, Lightweight Graph Capsule, Capsule Convolutional Network
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
*备注: 11 pages, 6 figures, accepted by AAAI-2025 conference

点击查看摘要

Abstract:Shared-account Sequential Recommendation (SSR) aims to provide personalized recommendations for accounts shared by multiple users with varying sequential preferences. Previous studies on SSR struggle to capture the fine-grained associations between interactions and different latent users within the shared account’s hybrid sequences. Moreover, most existing SSR methods (e.g., RNN-based or GCN-based methods) have quadratic computational complexities, hindering the deployment of SSRs on resource-constrained devices. To this end, we propose a Lightweight Graph Capsule Convolutional Network with subspace alignment for shared-account sequential recommendation, named LightGC ^2 N. Specifically, we devise a lightweight graph capsule convolutional network. It facilitates the fine-grained matching between interactions and latent users by attentively propagating messages on the capsule graphs. Besides, we present an efficient subspace alignment method. This method refines the sequence representations and then aligns them with the finely clustered preferences of latent users. The experimental results on four real-world datasets indicate that LightGC ^2 N outperforms nine state-of-the-art methods in accuracy and efficiency.

[AI-51] What Human-Horse Interactions may Teach us About Effective Human-AI Interactions

链接: https://arxiv.org/abs/2412.13405
作者: Mohammad Hossein Jarrahi,Stanley Ahalt
关键词: mutual adaptability, effective human-AI partnerships, explores human-horse interactions, article explores human-horse, designing effective human-AI
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
*备注:

点击查看摘要

Abstract:This article explores human-horse interactions as a metaphor for understanding and designing effective human-AI partnerships. Drawing on the long history of human collaboration with horses, we propose that AI, like horses, should complement rather than replace human capabilities. We move beyond traditional benchmarks such as the Turing test, which emphasize AI’s ability to mimic human intelligence, and instead advocate for a symbiotic relationship where distinct intelligences enhance each other. We analyze key elements of human-horse relationships: trust, communication, and mutual adaptability, to highlight essential principles for human-AI collaboration. Trust is critical in both partnerships, built through predictability and shared understanding, while communication and feedback loops foster mutual adaptability. We further discuss the importance of taming and habituation in shaping these interactions, likening it to how humans train AI to perform reliably and ethically in real-world settings. The article also addresses the asymmetry of responsibility, where humans ultimately bear the greater burden of oversight and ethical judgment. Finally, we emphasize that long-term commitment and continuous learning are vital in both human-horse and human-AI relationships, as ongoing interaction refines the partnership and increases mutual adaptability. By drawing on these insights from human-horse interactions, we offer a vision for designing AI systems that are trustworthy, adaptable, and capable of fostering symbiotic human-AI partnerships.

[AI-52] An Exploratory Study of ML Sketches and Visual Code Assistants

链接: https://arxiv.org/abs/2412.13386
作者: Luís F. Gomes,Vincent J. Hellendoorn,Jonathan Aldrich,Rui Abreu
关键词: Integrated Development Environments, Development Environments, Integrated Development, Visual Code Assistants, Code
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
*备注:

点击查看摘要

Abstract:This paper explores the integration of Visual Code Assistants in Integrated Development Environments (IDEs). In Software Engineering, whiteboard sketching is often the initial step before coding, serving as a crucial collaboration tool for developers. Previous studies have investigated patterns in SE sketches and how they are used in practice, yet methods for directly using these sketches for code generation remain limited. The emergence of visually-equipped large language models presents an opportunity to bridge this gap, which is the focus of our research. In this paper, we built a first prototype of a Visual Code Assistant to get user feedback regarding in-IDE sketch-to-code tools. We conduct an experiment with 19 data scientists, most of whom regularly sketch as part of their job. We investigate developers’ mental models by analyzing patterns commonly observed in their sketches when developing an ML workflow. Analysis indicates that diagrams were the preferred organizational component (52.6%), often accompanied by lists (42.1%) and numbered points (36.8%). Our tool converts their sketches into a Python notebook by querying an LLM. We use an LLM-as-judge setup to score the quality of the generated code, finding that even brief sketching can effectively generate useful code outlines. We also find a positive correlation between sketch time and the quality of the generated code. We conclude the study by conducting extensive interviews to assess the tool’s usefulness, explore potential use cases, and understand developers’ needs. As noted by participants, promising applications for these assistants include education, prototyping, and collaborative settings. Our findings signal promise for the next generation of Code Assistants to integrate visual information, both to improve code generation and to better leverage developers’ existing sketching practices.

[AI-53] Voter Priming Campaigns: Strategies Equilibria and Algorithms AAAI2025

链接: https://arxiv.org/abs/2412.13380
作者: Jonathan Shaki,Yonatan Aumann,Sarit Kraus
关键词: voters’ decisions, major determinant, determinant in voters’, parliamentary elections, elections
类目: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI)
*备注: To be published in AAAI 2025

点击查看摘要

Abstract:Issue salience is a major determinant in voters’ decisions. Candidates and political parties campaign to shift salience to their advantage - a process termed priming. We study the dynamics, strategies and equilibria of campaign spending for voter priming in multi-issue multi-party settings. We consider both parliamentary elections, where parties aim to maximize their share of votes, and various settings for presidential elections, where the winner takes all. For parliamentary elections, we show that pure equilibrium spending always exists and can be computed in time linear in the number of voters. For two parties and all settings, a spending equilibrium exists such that each party invests only in a single issue, and an equilibrium can be computed in time that is polynomial in the number of issues and linear in the number of voters. We also show that in most presidential settings no equilibrium exists. Additional properties of optimal campaign strategies are also studied.

[AI-54] Multiple Mean-Payoff Optimization under Local Stability Constraints AAAI2025

链接: https://arxiv.org/abs/2412.13369
作者: David Klaška,Antonín Kučera,Vojtěch Kůr,Vít Musil,Vojtěch Řehák
关键词: long-run average payoff, discrete systems, main tool, performance and dependability, dependability properties
类目: Artificial Intelligence (cs.AI)
*备注: Accepted to AAAI 2025

点击查看摘要

Abstract:The long-run average payoff per transition (mean payoff) is the main tool for specifying the performance and dependability properties of discrete systems. The problem of constructing a controller (strategy) simultaneously optimizing several mean payoffs has been deeply studied for stochastic and game-theoretic models. One common issue of the constructed controllers is the instability of the mean payoffs, measured by the deviations of the average rewards per transition computed in a finite “window” sliding along a run. Unfortunately, the problem of simultaneously optimizing the mean payoffs under local stability constraints is computationally hard, and the existing works do not provide a practically usable algorithm even for non-stochastic models such as two-player games. In this paper, we design and evaluate the first efficient and scalable solution to this problem applicable to Markov decision processes.

[AI-55] Quantitative Predictive Monitoring and Control for Safe Human-Machine Interaction

链接: https://arxiv.org/abs/2412.13365
作者: Shuyang Dong,Meiyi Ma,Josephine Lamp,Sebastian Elbaum,Matthew B. Dwyer,Lu Feng
关键词: healthcare and transportation, growing trend, systems interacting, revolutionize a range, range of application
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Systems and Control (eess.SY)
*备注:

点击查看摘要

Abstract:There is a growing trend toward AI systems interacting with humans to revolutionize a range of application domains such as healthcare and transportation. However, unsafe human-machine interaction can lead to catastrophic failures. We propose a novel approach that predicts future states by accounting for the uncertainty of human interaction, monitors whether predictions satisfy or violate safety requirements, and adapts control actions based on the predictive monitoring results. Specifically, we develop a new quantitative predictive monitor based on Signal Temporal Logic with Uncertainty (STL-U) to compute a robustness degree interval, which indicates the extent to which a sequence of uncertain predictions satisfies or violates an STL-U requirement. We also develop a new loss function to guide the uncertainty calibration of Bayesian deep learning and a new adaptive control method, both of which leverage STL-U quantitative predictive monitoring results. We apply the proposed approach to two case studies: Type 1 Diabetes management and semi-autonomous driving. Experiments show that the proposed approach improves safety and effectiveness in both case studies.

[AI-56] Multi-Agent Motion Planning For Differential Drive Robots Through Stationary State Search

链接: https://arxiv.org/abs/2412.13359
作者: Jingtian Yan,Jiaoyang Li
关键词: Multi-Agent Motion Planning, Motion Planning, airport operations, finds various applications, traffic management
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
*备注:

点击查看摘要

Abstract:Multi-Agent Motion Planning (MAMP) finds various applications in fields such as traffic management, airport operations, and warehouse automation. In many of these environments, differential drive robots are commonly used. These robots have a kinodynamic model that allows only in-place rotation and movement along their current orientation, subject to speed and acceleration limits. However, existing Multi-Agent Path Finding (MAPF)-based methods often use simplified models for robot kinodynamics, which limits their practicality and realism. In this paper, we introduce a three-level framework called MASS to address these challenges. MASS combines MAPF-based methods with our proposed stationary state search planner to generate high-quality kinodynamically-feasible plans. We further extend MASS using an adaptive window mechanism to address the lifelong MAMP problem. Empirically, we tested our methods on the single-shot grid map domain and the lifelong warehouse domain. Our method shows up to 400% improvements in terms of throughput compared to existing methods.

[AI-57] A Novel Machine Learning Classifier Based on Genetic Algorithms and Data Importance Reformatting

链接: https://arxiv.org/abs/2412.13350
作者: A. K. Alkhayyata,N. M. Hewahi
关键词: Genetic Algorithms, Data Importance, data reformatting phase, Machine Learning, classification algorithm
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
*备注:

点击查看摘要

Abstract:In this paper, a novel classification algorithm that is based on Data Importance (DI) reformatting and Genetic Algorithms (GA) named GADIC is proposed to overcome the issues related to the nature of data which may hinder the performance of the Machine Learning (ML) classifiers. GADIC comprises three phases which are data reformatting phase which depends on DI concept, training phase where GA is applied on the reformatted training dataset, and testing phase where the instances of the reformatted testing dataset are being averaged based on similar instances in the training dataset. GADIC is an approach that utilizes the exiting ML classifiers with involvement of data reformatting, using GA to tune the inputs, and averaging the similar instances to the unknown instance. The averaging of the instances becomes the unknown instance to be classified in the stage of testing. GADIC has been tested on five existing ML classifiers which are Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Logistic Regression (LR), Decision Tree (DT), and Naïve Bayes (NB). All were evaluated using seven open-source UCI ML repository and Kaggle datasets which are Cleveland heart disease, Indian liver patient, Pima Indian diabetes, employee future prediction, telecom churn prediction, bank customer churn, and tech students. In terms of accuracy, the results showed that, with the exception of approximately 1% decrease in the accuracy of NB classifier in Cleveland heart disease dataset, GADIC significantly enhanced the performance of most ML classifiers using various datasets. In addition, KNN with GADIC showed the greatest performance gain when compared with other ML classifiers with GADIC followed by SVM while LR had the lowest improvement. The lowest average improvement that GADIC could achieve is 5.96%, whereas the maximum average improvement reached 16.79%.

[AI-58] Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLM s ICLR2025

链接: https://arxiv.org/abs/2412.13337
作者: Aldo Pareja,Nikhil Shivakumar Nayak,Hao Wang,Krishnateja Killamsetty,Shivchander Sudalairaj,Wenlong Zhao,Seungwook Han,Abhishek Bhandwaldar,Guangxuan Xu,Kai Xu,Ligong Han,Luke Inglis,Akash Srivastava
关键词: organizations face barriers, face barriers due, effectively fine-tune LLMs, large language models, industrial research labs
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
*备注: 33 pages, 19 figures. Appendix included in submission. Submitted to ICLR 2025

点击查看摘要

Abstract:The rise of large language models (LLMs) has created a significant disparity: industrial research labs with their computational resources, expert teams, and advanced infrastructures, can effectively fine-tune LLMs, while individual developers and small organizations face barriers due to limited resources. In this paper, we aim to bridge this gap by presenting a comprehensive study on supervised fine-tuning of LLMs using instruction-tuning datasets spanning diverse knowledge domains and skills. We focus on small-sized LLMs (3B to 7B parameters) for their cost-efficiency and accessibility. We explore various training configurations and strategies across four open-source pre-trained models. We provide detailed documentation of these configurations, revealing findings that challenge several common training practices, including hyperparameter recommendations from TULU and phased training recommended by Orca. Key insights from our work include: (i) larger batch sizes paired with lower learning rates lead to improved model performance on benchmarks such as MMLU, MTBench, and Open LLM Leaderboard; (ii) early-stage training dynamics, such as lower gradient norms and higher loss values, are strong indicators of better final model performance, enabling early termination of sub-optimal runs and significant computational savings; (iii) through a thorough exploration of hyperparameters like warmup steps and learning rate schedules, we provide guidance for practitioners and find that certain simplifications do not compromise performance; and (iv) we observed no significant difference in performance between phased and stacked training strategies, but stacked training is simpler and more sample efficient. With these findings holding robustly across datasets and models, we hope this study serves as a guide for practitioners fine-tuning small LLMs and promotes a more inclusive environment for LLM research.

[AI-59] Predictive Probability Density Mapping for Search and Rescue Using An Agent -Based Approach with Sparse Data

链接: https://arxiv.org/abs/2412.13317
作者: Jan-Hendrik Ewers,David Anderson,Douglas Thomson
关键词: lost person, limited resources, found is crucial, search and rescue, lost person emerges
类目: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
*备注:

点击查看摘要

Abstract:Predicting the location where a lost person could be found is crucial for search and rescue operations with limited resources. To improve the precision and efficiency of these predictions, simulated agents can be created to emulate the behavior of the lost person. Within this study, we introduce an innovative agent-based model designed to replicate diverse psychological profiles of lost persons, allowing these agents to navigate real-world landscapes while making decisions autonomously without the need for location-specific training. The probability distribution map depicting the potential location of the lost person emerges through a combination of Monte Carlo simulations and mobility-time-based sampling. Validation of the model is achieved using real-world Search and Rescue data to train a Gaussian Process model. This allows generalization of the data to sample initial starting points for the agents during validation. Comparative analysis with historical data showcases promising outcomes relative to alternative methods. This work introduces a flexible agent that can be employed in search and rescue operations, offering adaptability across various geographical locations.

[AI-60] Posterior Mean Matching: Generative Modeling through Online Bayesian Inference

链接: https://arxiv.org/abs/2412.13286
作者: Sebastian Salazar,Michal Kucer,Yixin Wang,Emily Casleton,David Blei
关键词: paper introduces posterior, generative PMM model, PMM, PMM model, generative PMM
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
*备注:

点击查看摘要

Abstract:This paper introduces posterior mean matching (PMM), a new method for generative modeling that is grounded in Bayesian inference. PMM uses conjugate pairs of distributions to model complex data of various modalities like images and text, offering a flexible alternative to existing methods like diffusion models. PMM models iteratively refine noisy approximations of the target distribution using updates from online Bayesian inference. PMM is flexible because its mechanics are based on general Bayesian models. We demonstrate this flexibility by developing specialized examples: a generative PMM model of real-valued data using the Normal-Normal model, a generative PMM model of count data using a Gamma-Poisson model, and a generative PMM model of discrete data using a Dirichlet-Categorical model. For the Normal-Normal PMM model, we establish a direct connection to diffusion models by showing that its continuous-time formulation converges to a stochastic differential equation (SDE). Additionally, for the Gamma-Poisson PMM, we derive a novel SDE driven by a Cox process, which is a significant departure from traditional Brownian motion-based generative models. PMMs achieve performance that is competitive with generative models for language modeling and image generation.

[AI-61] Enhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks

链接: https://arxiv.org/abs/2412.13240
作者: Safa Ben Atitallah,Maha Driss,Wadii Boulila,Anis Koubaa
关键词: Internet of Things, Convolutional Neural Networks, ensuring the security, rapid rise, Markov Graph Convolutional
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
*备注:

点击查看摘要

Abstract:With the rapid rise of the Internet of Things (IoT), ensuring the security of IoT devices has become essential. One of the primary challenges in this field is that new types of attacks often have significantly fewer samples than more common attacks, leading to unbalanced datasets. Existing research on detecting intrusions in these unbalanced labeled datasets primarily employs Convolutional Neural Networks (CNNs) or conventional Machine Learning (ML) models, which result in incomplete detection, especially for new attacks. To handle these challenges, we suggest a new approach to IoT intrusion detection using Self-Supervised Learning (SSL) with a Markov Graph Convolutional Network (MarkovGCN). Graph learning excels at modeling complex relationships within data, while SSL mitigates the issue of limited labeled data for emerging attacks. Our approach leverages the inherent structure of IoT networks to pre-train a GCN, which is then fine-tuned for the intrusion detection task. The integration of Markov chains in GCN uncovers network structures and enriches node and edge features with contextual information. Experimental results demonstrate that our approach significantly improves detection accuracy and robustness compared to conventional supervised learning methods. Using the EdgeIIoT-set dataset, we attained an accuracy of 98.68%, a precision of 98.18%, a recall of 98.35%, and an F1-Score of 98.40%.

[AI-62] SafeDrive: Knowledge- and Data-Driven Risk-Sensitive Decision-Making for Autonomous Vehicles with Large Language Models

链接: https://arxiv.org/abs/2412.13238
作者: Zhiyuan Zhou,Heye Huang,Boqi Li,Shiyue Zhao,Yao Mu
关键词: Large Language Models, Language Models, Large Language, Recent advancements, Module
类目: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Robotics (cs.RO)
*备注:

点击查看摘要

Abstract:Recent advancements in autonomous vehicles (AVs) use Large Language Models (LLMs) to perform well in normal driving scenarios. However, ensuring safety in dynamic, high-risk environments and managing safety-critical long-tail events remain significant challenges. To address these issues, we propose SafeDrive, a knowledge- and data-driven risk-sensitive decision-making framework to enhance AV safety and adaptability. The proposed framework introduces a modular system comprising: (1) a Risk Module for quantifying multi-factor coupled risks involving driver, vehicle, and road interactions; (2) a Memory Module for storing and retrieving typical scenarios to improve adaptability; (3) a LLM-powered Reasoning Module for context-aware safety decision-making; and (4) a Reflection Module for refining decisions through iterative learning. By integrating knowledge-driven insights with adaptive learning mechanisms, the framework ensures robust decision-making under uncertain conditions. Extensive evaluations on real-world traffic datasets, including highways (HighD), intersections (InD), and roundabouts (RounD), validate the framework’s ability to enhance decision-making safety (achieving a 100% safety rate), replicate human-like driving behaviors (with decision alignment exceeding 85%), and adapt effectively to unpredictable scenarios. SafeDrive establishes a novel paradigm for integrating knowledge- and data-driven methods, highlighting significant potential to improve safety and adaptability of autonomous driving in high-risk traffic scenarios.

[AI-63] COSEE: Consistency-Oriented Signal-Based Early Exiting via Calibrated Sample Weighting Mechanism AAAI2025

链接: https://arxiv.org/abs/2412.13236
作者: Jianing He,Qi Zhang,Hongyun Zhang,Xuanjing Huang,Usman Naseem,Duoqian Miao
关键词: pre-trained language models, Early exiting, Signal-based Early Exiting, early exiting behavior, test-time early exiting
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
*备注: AAAI 2025, 11 pages

点击查看摘要

Abstract:Early exiting is an effective paradigm for improving the inference efficiency of pre-trained language models (PLMs) by dynamically adjusting the number of executed layers for each sample. However, in most existing works, easy and hard samples are treated equally by each classifier during training, which neglects the test-time early exiting behavior, leading to inconsistency between training and testing. Although some methods have tackled this issue under a fixed speed-up ratio, the challenge of flexibly adjusting the speed-up ratio while maintaining consistency between training and testing is still under-explored. To bridge the gap, we propose a novel Consistency-Oriented Signal-based Early Exiting (COSEE) framework, which leverages a calibrated sample weighting mechanism to enable each classifier to emphasize the samples that are more likely to exit at that classifier under various acceleration scenarios. Extensive experiments on the GLUE benchmark demonstrate the effectiveness of our COSEE across multiple exiting signals and backbones, yielding a better trade-off between performance and efficiency.

[AI-64] Logic-Constrained Shortest Paths for Flight Planning

链接: https://arxiv.org/abs/2412.13235
作者: Ricardo Euler,Pedro Maristany de las Casas,Ralf Borndörfer
关键词: Shortest Path Problem, Logic-Constrained Shortest Path, Shortest Path, satisfiability constraints imposed, Path Problem
类目: Artificial Intelligence (cs.AI); Discrete Mathematics (cs.DM)
*备注:

点击查看摘要

Abstract:The Logic-Constrained Shortest Path Problem (LCSP) combines a one-to-one shortest path problem with satisfiability constraints imposed on the routing graph. This setting arises in flight planning, where air traffic control (ATC) authorities are enforcing a set of traffic flow restrictions (TFRs) on aircraft routes in order to increase safety and throughput. We propose a new branch and bound-based algorithm for the LCSP. The resulting algorithm has three main degrees of freedom: the node selection rule, the branching rule and the conflict. While node selection and branching rules have been long studied in the MIP and SAT communities, most of them cannot be applied out of the box for the LCSP. We review the existing literature and develop tailored variants of the most prominent rules. The conflict, the set of variables to which the branching rule is applied, is unique to the LCSP. We analyze its theoretical impact on the BB algorithm. In the second part of the paper, we show how to model the Flight Planning Problem with TFRs as an LCSP and solve it using the branch and bound algorithm. We demonstrate the algorithm’s efficiency on a dataset consisting of a global flight graph and a set of around 20000 real TFRs obtained from our industry partner Lufthansa Systems GmbH. We make this dataset publicly available. Finally, we conduct an empirical in-depth analysis of node selection rules, branching rules and conflicts. Carefully choosing an appropriate combination yields an improvement of an order of magnitude compared to an uninformed choice. Subjects: Artificial Intelligence (cs.AI); Discrete Mathematics (cs.DM) Cite as: arXiv:2412.13235 [cs.AI] (or arXiv:2412.13235v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2412.13235 Focus to learn more arXiv-issued DOI via DataCite

[AI-65] C2F-TP: A Coarse-to-Fine Denoising Framework for Uncertainty-Aware Trajectory Prediction

链接: https://arxiv.org/abs/2412.13231
作者: Zichen Wang,Hao Miao,Senzhang Wang,Renzhi Wang,Jianxin Wang,Jian Zhang
关键词: Accurately predicting, critically important, important for ensuring, ensuring safety, safety and reliability
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Accurately predicting the trajectory of vehicles is critically important for ensuring safety and reliability in autonomous driving. Although considerable research efforts have been made recently, the inherent trajectory uncertainty caused by various factors including the dynamic driving intends and the diverse driving scenarios still poses significant challenges to accurate trajectory prediction. To address this issue, we propose C2F-TP, a coarse-to-fine denoising framework for uncertainty-aware vehicle trajectory prediction. C2F-TP features an innovative two-stage coarse-to-fine prediction process. Specifically, in the spatial-temporal interaction stage, we propose a spatial-temporal interaction module to capture the inter-vehicle interactions and learn a multimodal trajectory distribution, from which a certain number of noisy trajectories are sampled. Next, in the trajectory refinement stage, we design a conditional denoising model to reduce the uncertainty of the sampled trajectories through a step-wise denoising operation. Extensive experiments are conducted on two real datasets NGSIM and highD that are widely adopted in trajectory prediction. The result demonstrates the effectiveness of our proposal.

[AI-66] raining Verification-Friendly Neural Networks via Neuron Behavior Consistency AAAI2025

链接: https://arxiv.org/abs/2412.13229
作者: Zongxin Liu,Zhe Zhao,Fu Song,Jun Sun,Pengfei Yang,Xiaowei Huang,Lijun Zhang
关键词: long verification time, critical security assurances, practical application suffers, Formal verification, verification time
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
*备注: Accpeted by AAAI2025

点击查看摘要

Abstract:Formal verification provides critical security assurances for neural networks, yet its practical application suffers from the long verification time. This work introduces a novel method for training verification-friendly neural networks, which are robust, easy to verify, and relatively accurate. Our method integrates neuron behavior consistency into the training process, making neuron activation states consistent across different inputs in a local neighborhood, reducing the number of unstable neurons and tightening the bounds of neurons thereby enhancing neural network verifiability. We evaluated our method using the MNIST, Fashion-MNIST, and CIFAR-10 datasets across various network architectures. The results of the experiment demonstrate that networks trained using our method are verification-friendly across different radii and different model architectures, whereas other tools fail to maintain verifiability as the radius increases. We also show that our method can be combined with existing methods to further improve the verifiability of networks.

[AI-67] Physics-model-guided Worst-case Sampling for Safe Reinforcement Learning

链接: https://arxiv.org/abs/2412.13224
作者: Hongpeng Cao,Yanbing Mao,Lui Sha,Marco Caccamo
关键词: learning-enabled CPS frequently, CPS frequently occur, Real-world accidents, accidents in learning-enabled, frequently occur
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
*备注: under review

点击查看摘要

Abstract:Real-world accidents in learning-enabled CPS frequently occur in challenging corner cases. During the training of deep reinforcement learning (DRL) policy, the standard setup for training conditions is either fixed at a single initial condition or uniformly sampled from the admissible state space. This setup often overlooks the challenging but safety-critical corner cases. To bridge this gap, this paper proposes a physics-model-guided worst-case sampling strategy for training safe policies that can handle safety-critical cases toward guaranteed safety. Furthermore, we integrate the proposed worst-case sampling strategy into the physics-regulated deep reinforcement learning (Phy-DRL) framework to build a more data-efficient and safe learning algorithm for safety-critical CPS. We validate the proposed training strategy with Phy-DRL through extensive experiments on a simulated cart-pole system, a 2D quadrotor, a simulated and a real quadruped robot, showing remarkably improved sampling efficiency to learn more robust safe policies.

[AI-68] An introduction to reservoir computing

链接: https://arxiv.org/abs/2412.13212
作者: Michael te Vrugt
关键词: artificial neural networks, growing interest, development of artificial, artificial neural, reservoir computing
类目: Emerging Technologies (cs.ET); Disordered Systems and Neural Networks (cond-mat.dis-nn); Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI); Applied Physics (physics.app-ph); Quantum Physics (quant-ph)
*备注: Book chapter, to appear in: Artificial Intelligence and Intelligent Matter, Springer, Cham

点击查看摘要

Abstract:There is a growing interest in the development of artificial neural networks that are implemented in a physical system. A major challenge in this context is that these networks are difficult to train since training here would require a change of physical parameters rather than simply of coefficients in a computer program. For this reason, reservoir computing, where one employs high-dimensional recurrent networks and trains only the final layer, is widely used in this context. In this chapter, I introduce the basic concepts of reservoir computing. Moreover, I present some important physical implementations coming from electronics, photonics, spintronics, mechanics, and biology. Finally, I provide a brief discussion of quantum reservoir computing.

[AI-69] Gauss-Newton Dynamics for Neural Networks: A Riemannian Optimization Perspective

链接: https://arxiv.org/abs/2412.14031
作者: Semih Cayci
关键词: smooth activation functions, Riemannian gradient flow, Gauss-Newton gradient flow, training neural networks, Riemannian gradient
类目: Optimization and Control (math.OC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Systems and Control (eess.SY); Machine Learning (stat.ML)
*备注:

点击查看摘要

Abstract:We analyze the convergence of Gauss-Newton dynamics for training neural networks with smooth activation functions. In the underparameterized regime, the Gauss-Newton gradient flow induces a Riemannian gradient flow on a low-dimensional, smooth, embedded submanifold of the Euclidean output space. Using tools from Riemannian optimization, we prove \emphlast-iterate convergence of the Riemannian gradient flow to the optimal in-class predictor at an \emphexponential rate that is independent of the conditioning of the Gram matrix, \emphwithout requiring explicit regularization. We further characterize the critical impacts of the neural network scaling factor and the initialization on the convergence behavior. In the overparameterized regime, we show that the Levenberg-Marquardt dynamics with an appropriately chosen damping factor yields robustness to ill-conditioned kernels, analogous to the underparameterized regime. These findings demonstrate the potential of Gauss-Newton methods for efficiently optimizing neural networks, particularly in ill-conditioned problems where kernel and Gram matrices have small singular values.

[AI-70] AI-Powered Algorithm-Centric Quantum Processor Topology Design AAAI2025

链接: https://arxiv.org/abs/2412.13805
作者: Tian Li,Xiao-Yue Xu,Chen Ding,Tian-Ci Tian,Wei-You Liao,Shuo Zhang,He-Liang Huang
关键词: effective compilation process, Quantum computing promises, quantum programs necessitates, Quantum, compilation process
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI)
*备注: Accepted by AAAI 2025

点击查看摘要

Abstract:Quantum computing promises to revolutionize various fields, yet the execution of quantum programs necessitates an effective compilation process. This involves strategically mapping quantum circuits onto the physical qubits of a quantum processor. The qubits’ arrangement, or topology, is pivotal to the circuit’s performance, a factor that often defies traditional heuristic or manual optimization methods due to its complexity. In this study, we introduce a novel approach leveraging reinforcement learning to dynamically tailor qubit topologies to the unique specifications of individual quantum circuits, guiding algorithm-driven quantum processor topology design for reducing the depth of mapped circuit, which is particularly critical for the output accuracy on noisy quantum processors. Our method marks a significant departure from previous methods that have been constrained to mapping circuits onto a fixed processor topology. Experiments demonstrate that we have achieved notable enhancements in circuit performance, with a minimum of 20% reduction in circuit depth in 60% of the cases examined, and a maximum enhancement of up to 46%. Furthermore, the pronounced benefits of our approach in reducing circuit depth become increasingly evident as the scale of the quantum circuits increases, exhibiting the scalability of our method in terms of problem size. This work advances the co-design of quantum processor architecture and algorithm mapping, offering a promising avenue for future research and development in the field.

[AI-71] QuLTSF: Long-Term Time Series Forecasting with Quantum Machine Learning

链接: https://arxiv.org/abs/2412.13769
作者: Hari Hara Suthan Chittoor,Paul Robert Griffin,Ariel Neufeld,Jayne Thompson,Mile Gu
关键词: Long-term time series, time series forecasting, stock market analysis, disease outbreak prediction, Long-term time
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
*备注: submitted for conference publication

点击查看摘要

Abstract:Long-term time series forecasting (LTSF) involves predicting a large number of future values of a time series based on the past values and is an essential task in a wide range of domains including weather forecasting, stock market analysis, disease outbreak prediction. Over the decades LTSF algorithms have transitioned from statistical models to deep learning models like transformer models. Despite the complex architecture of transformer based LTSF models `Are Transformers Effective for Time Series Forecasting? (Zeng et al., 2023)’ showed that simple linear models can outperform the state-of-the-art transformer based LTSF models. Recently, quantum machine learning (QML) is evolving as a domain to enhance the capabilities of classical machine learning models. In this paper we initiate the application of QML to LTSF problems by proposing QuLTSF, a simple hybrid QML model for multivariate LTSF. Through extensive experiments on a widely used weather dataset we show the advantages of QuLTSF over the state-of-the-art classical linear models, in terms of reduced mean squared error and mean absolute error.

[AI-72] SEML: A task-specific embedding-based method for few-shot classification of cancer molecular subtypes

链接: https://arxiv.org/abs/2412.13228
作者: Ran Sua,Rui Shi,Hui Cui,Ping Xuan,Chengyan Fang,Xikang Feng,Qiangguo Jin
关键词: challenging upstream task, critical and challenging, challenging upstream, molecular subtype, Molecular
类目: Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Molecular subtyping of cancer is recognized as a critical and challenging upstream task for personalized therapy. Existing deep learning methods have achieved significant performance in this domain when abundant data samples are available. However, the acquisition of densely labeled samples for cancer molecular subtypes remains a significant challenge for conventional data-intensive deep learning approaches. In this work, we focus on the few-shot molecular subtype prediction problem in heterogeneous and small cancer datasets, aiming to enhance precise diagnosis and personalized treatment. We first construct a new few-shot dataset for cancer molecular subtype classification and auxiliary cancer classification, named TCGA Few-Shot, from existing publicly available datasets. To effectively leverage the relevant knowledge from both tasks, we introduce a task-specific embedding-based meta-learning framework (TSEML). TSEML leverages the synergistic strengths of a model-agnostic meta-learning (MAML) approach and a prototypical network (ProtoNet) to capture diverse and fine-grained features. Comparative experiments conducted on the TCGA Few-Shot dataset demonstrate that our TSEML framework achieves superior performance in addressing the problem of few-shot molecular subtype classification.

[AI-73] Generative modeling of protein ensembles guided by crystallographic electron densities

链接: https://arxiv.org/abs/2412.13223
作者: Sai Advaith Maddipatla,Nadav Bojan Sellam,Sanketh Vedula,Ailie Marx,Alex Bronstein
关键词: X-ray crystallography experiments, adopting ensembles, Abstract, obtained from X-ray, X-ray crystallography
类目: Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Proteins are dynamic, adopting ensembles of conformations. The nature of this conformational heterogenity is imprinted in the raw electron density measurements obtained from X-ray crystallography experiments. Fitting an ensemble of protein structures to these measurements is a challenging, ill-posed inverse problem. We propose a non-i.i.d. ensemble guidance approach to solve this problem using existing protein structure generative models and demonstrate that it accurately recovers complicated multi-modal alternate protein backbone conformations observed in certain single crystal measurements.

机器学习

[LG-0] On Calibration in Multi-Distribution Learning

链接: https://arxiv.org/abs/2412.14142
作者: Rajeev Verma,Volker Fischer,Eric Nalisnick
关键词: Modern challenges, multiple distributions, machine learning, multi-distribution learning, challenges of robustness
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Modern challenges of robustness, fairness, and decision-making in machine learning have led to the formulation of multi-distribution learning (MDL) frameworks in which a predictor is optimized across multiple distributions. We study the calibration properties of MDL to better understand how the predictor performs uniformly across the multiple distributions. Through classical results on decomposing proper scoring losses, we first derive the Bayes optimal rule for MDL, demonstrating that it maximizes the generalized entropy of the associated loss function. Our analysis reveals that while this approach ensures minimal worst-case loss, it can lead to non-uniform calibration errors across the multiple distributions and there is an inherent calibration-refinement trade-off, even at Bayes optimality. Our results highlight a critical limitation: despite the promise of MDL, one must use caution when designing predictors tailored to multiple distributions so as to minimize disparity.

[LG-1] rustworthy Transfer Learning: A Survey

链接: https://arxiv.org/abs/2412.14116
作者: Jun Wu,Jingrui He
关键词: relevant target domain, Transfer learning, Transfer, Transfer learning aims, relevant target
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Transfer learning aims to transfer knowledge or information from a source domain to a relevant target domain. In this paper, we understand transfer learning from the perspectives of knowledge transferability and trustworthiness. This involves two research questions: How is knowledge transferability quantitatively measured and enhanced across domains? Can we trust the transferred knowledge in the transfer learning process? To answer these questions, this paper provides a comprehensive review of trustworthy transfer learning from various aspects, including problem definitions, theoretical analysis, empirical algorithms, and real-world applications. Specifically, we summarize recent theories and algorithms for understanding knowledge transferability under (within-domain) IID and non-IID assumptions. In addition to knowledge transferability, we review the impact of trustworthiness on transfer learning, e.g., whether the transferred knowledge is adversarially robust or algorithmically fair, how to transfer the knowledge under privacy-preserving constraints, etc. Beyond discussing the current advancements, we highlight the open questions and future directions for understanding transfer learning in a reliable and trustworthy manner.

[LG-2] Machine Learning Co-pilot for Screening of Organic Molecular Additives for Perovskite Solar Cells

链接: https://arxiv.org/abs/2412.14109
作者: Yang Pu,Zhiyuan Dai,Yifan Zhou,Ning Jia,Hongyue Wang,Yerzhan Mukhametkarimov,Ruihao Chen,Hongqiang Wang,Zhe Liu
关键词: planar perovskite photovoltaics, Machine learning, Perovskite Additive Screener, encountering predictive biases, screen effective organic
类目: Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci); Applied Physics (physics.app-ph)
*备注:

点击查看摘要

Abstract:Machine learning (ML) has been extensively employed in planar perovskite photovoltaics to screen effective organic molecular additives, while encountering predictive biases for novel materials due to small datasets and reliance on predefined descriptors. Present work thus proposes an effective approach, Co-Pilot for Perovskite Additive Screener (Co-PAS), an ML-driven framework designed to accelerate additive screening for perovskite solar cells (PSCs). Co-PAS overcomes predictive biases by integrating the Molecular Scaffold Classifier (MSC) for scaffold-based pre-screening and utilizing Junction Tree Variational Autoencoder (JTVAE) latent vectors to enhance molecular structure representation, thereby enhancing the accuracy of power conversion efficiency (PCE) predictions. Leveraging Co-PAS, we integrate domain knowledge to screen an extensive dataset of 250,000 molecules from PubChem, prioritizing candidates based on predicted PCE values and key molecular properties such as donor number, dipole moment, and hydrogen bond acceptor count. This workflow leads to the identification of several promising passivating molecules, including the novel Boc-L-threonine N-hydroxysuccinimide ester (BTN), which, to our knowledge, has not been explored as an additive in PSCs and achieves a device PCE of 25.20%. Our results underscore the potential of Co-PAS in advancing additive discovery for high-performance PSCs.

[LG-3] On the Robustness of Distributed Machine Learning against Transfer Attacks AAAI

链接: https://arxiv.org/abs/2412.14080
作者: Sébastien Andreina,Pascal Zimmer,Ghassan Karame
关键词: gaining considerable attention, distributed machine learning, gaining considerable, considerable attention, independently looked
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注: To appear in the Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) 2025

点击查看摘要

Abstract:Although distributed machine learning (distributed ML) is gaining considerable attention in the community, prior works have independently looked at instances of distributed ML in either the training or the inference phase. No prior work has examined the combined robustness stemming from distributing both the learning and the inference process. In this work, we explore, for the first time, the robustness of distributed ML models that are fully heterogeneous in training data, architecture, scheduler, optimizer, and other model parameters. Supported by theory and extensive experimental validation using CIFAR10 and FashionMNIST, we show that such properly distributed ML instantiations achieve across-the-board improvements in accuracy-robustness tradeoffs against state-of-the-art transfer-based attacks that could otherwise not be realized by current ensemble or federated learning instantiations. For instance, our experiments on CIFAR10 show that for the Common Weakness attack, one of the most powerful state-of-the-art transfer-based attacks, our method improves robust accuracy by up to 40%, with a minimal impact on clean task accuracy.

[LG-4] Online MDP with Transition Prototypes: A Robust Adaptive Approach

链接: https://arxiv.org/abs/2412.14075
作者: Shuo Sun,Meng Qi,Zuo-jun Max Shen
关键词: Markov Decision Process, robust Markov Decision, Decision Process, Markov Decision, underlying transition kernel
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:In this work, we consider an online robust Markov Decision Process (MDP) where we have the information of finitely many prototypes of the underlying transition kernel. We consider an adaptively updated ambiguity set of the prototypes and propose an algorithm that efficiently identifies the true underlying transition kernel while guaranteeing the performance of the corresponding robust policy. To be more specific, we provide a sublinear regret of the subsequent optimal robust policy. We also provide an early stopping mechanism and a worst-case performance bound of the value function. In numerical experiments, we demonstrate that our method outperforms existing approaches, particularly in the early stage with limited data. This work contributes to robust MDPs by considering possible prior information about the underlying transition probability and online learning, offering both theoretical insights and practical algorithms for improved decision-making under uncertainty.

[LG-5] Evidential Deep Learning for Probabilistic Modelling of Extreme Storm Events

链接: https://arxiv.org/abs/2412.14048
作者: Ayush Khot,Xihaier Luo,Ai Kagawa,Shinjae Yoo
关键词: play an important, important role, role in reducing, reducing errors, Uncertainty quantification
类目: Machine Learning (cs.LG)
*备注: 14 pages, 10 figures

点击查看摘要

Abstract:Uncertainty quantification (UQ) methods play an important role in reducing errors in weather forecasting. Conventional approaches in UQ for weather forecasting rely on generating an ensemble of forecasts from physics-based simulations to estimate the uncertainty. However, it is computationally expensive to generate many forecasts to predict real-time extreme weather events. Evidential Deep Learning (EDL) is an uncertainty-aware deep learning approach designed to provide confidence about its predictions using only one forecast. It treats learning as an evidence acquisition process where more evidence is interpreted as increased predictive confidence. We apply EDL to storm forecasting using real-world weather datasets and compare its performance with traditional methods. Our findings indicate that EDL not only reduces computational overhead but also enhances predictive uncertainty. This method opens up novel opportunities in research areas such as climate risk assessment, where quantifying the uncertainty about future climate is crucial.

[LG-6] Machine learning in wastewater treatment: insights from modelling a pilot denitrification reactor

链接: https://arxiv.org/abs/2412.14030
作者: Eivind Bøhn,Sølve Eidnes,Kjell Rune Jonassen
关键词: Wastewater treatment plants, machine learning applications, plants are increasingly, increasingly recognized, recognized as promising
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Wastewater treatment plants are increasingly recognized as promising candidates for machine learning applications, due to their societal importance and high availability of data. However, their varied designs, operational conditions, and influent characteristics hinder straightforward automation. In this study, we use data from a pilot reactor at the Veas treatment facility in Norway to explore how machine learning can be used to optimize biological nitrate ( \mathrmNO_3^- ) reduction to molecular nitrogen ( \mathrmN_2 ) in the biogeochemical process known as \textitdenitrification. Rather than focusing solely on predictive accuracy, our approach prioritizes understanding the foundational requirements for effective data-driven modelling of wastewater treatment. Specifically, we aim to identify which process parameters are most critical, the necessary data quantity and quality, how to structure data effectively, and what properties are required by the models. We find that nonlinear models perform best on the training and validation data sets, indicating nonlinear relationships to be learned, but linear models transfer better to the unseen test data, which comes later in time. The variable measuring the water temperature has a particularly detrimental effect on the models, owing to a significant change in distributions between training and test data. We therefore conclude that multiple years of data is necessary to learn robust machine learning models. By addressing foundational elements, particularly in the context of the climatic variability faced by northern regions, this work lays the groundwork for a more structured and tailored approach to machine learning for wastewater treatment. We share publicly both the data and code used to produce the results in the paper.

[LG-7] Flow Exporter Impact on Intelligent Intrusion Detection Systems

链接: https://arxiv.org/abs/2412.14021
作者: Daniela Pinto,João Vitorino,Eva Maia,Ivone Amorim,Isabel Praça
关键词: High-quality datasets, training machine learning, machine learning models, critical for training, intrusion detection datasets
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: 9 pages, 10 tables, ICISSP 2025 conference

点击查看摘要

Abstract:High-quality datasets are critical for training machine learning models, as inconsistencies in feature generation can hinder the accuracy and reliability of threat detection. For this reason, ensuring the quality of the data in network intrusion detection datasets is important. A key component of this is using reliable tools to generate the flows and features present in the datasets. This paper investigates the impact of flow exporters on the performance and reliability of machine learning models for intrusion detection. Using HERA, a tool designed to export flows and extract features, the raw network packets of two widely used datasets, UNSW-NB15 and CIC-IDS2017, were processed from PCAP files to generate new versions of these datasets. These were compared to the original ones in terms of their influence on the performance of several models, including Random Forest, XGBoost, LightGBM, and Explainable Boosting Machine. The results obtained were significant. Models trained on the HERA version of the datasets consistently outperformed those trained on the original dataset, showing improvements in accuracy and indicating a better generalisation. This highlighted the importance of flow generation in the model’s ability to differentiate between benign and malicious traffic.

[LG-8] Modality-Independent Graph Neural Networks with Global Transformers for Multimodal Recommendation AAAI2025

链接: https://arxiv.org/abs/2412.13994
作者: Jun Hu,Bryan Hooi,Bingsheng He,Yinwei Wei
关键词: learn users’ preferences, Multimodal recommendation systems, receptive fields, Multimodal recommendation, existing user-item interactions
类目: ocial and Information Networks (cs.SI); Machine Learning (cs.LG)
*备注: Accepted by AAAI 2025

点击查看摘要

Abstract:Multimodal recommendation systems can learn users’ preferences from existing user-item interactions as well as the semantics of multimodal data associated with items. Many existing methods model this through a multimodal user-item graph, approaching multimodal recommendation as a graph learning task. Graph Neural Networks (GNNs) have shown promising performance in this domain. Prior research has capitalized on GNNs’ capability to capture neighborhood information within certain receptive fields (typically denoted by the number of hops, K ) to enrich user and item semantics. We observe that the optimal receptive fields for GNNs can vary across different modalities. In this paper, we propose GNNs with Modality-Independent Receptive Fields, which employ separate GNNs with independent receptive fields for different modalities to enhance performance. Our results indicate that the optimal K for certain modalities on specific datasets can be as low as 1 or 2, which may restrict the GNNs’ capacity to capture global information. To address this, we introduce a Sampling-based Global Transformer, which utilizes uniform global sampling to effectively integrate global information for GNNs. We conduct comprehensive experiments that demonstrate the superiority of our approach over existing methods. Our code is publicly available at this https URL.

[LG-9] RAG for Effective Supply Chain Security Questionnaire Automation

链接: https://arxiv.org/abs/2412.13988
作者: Zaynab Batool Reza,Abdul Rafay Syed,Omer Iqbal,Ethel Mensah,Qian Liu,Maxx Richard Rahman,Wolfgang Maass
关键词: Natural Language Processing, supply chain security, chain security questionnaires, questionnaires is imperative, efficient processing
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:In an era where digital security is crucial, efficient processing of security-related inquiries through supply chain security questionnaires is imperative. This paper introduces a novel approach using Natural Language Processing (NLP) and Retrieval-Augmented Generation (RAG) to automate these responses. We developed QuestSecure, a system that interprets diverse document formats and generates precise responses by integrating large language models (LLMs) with an advanced retrieval system. Our experiments show that QuestSecure significantly improves response accuracy and operational efficiency. By employing advanced NLP techniques and tailored retrieval mechanisms, the system consistently produces contextually relevant and semantically rich responses, reducing cognitive load on security teams and minimizing potential errors. This research offers promising avenues for automating complex security management tasks, enhancing organizational security processes.

[LG-10] Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates

链接: https://arxiv.org/abs/2412.13966
作者: Sen Yan,David J. O’Connor,Xiaojun Wang,Noel E. O’Connor,Alan. F. Smeaton,Mingming Liu
关键词: Urban pollution poses, traffic-related air pollution, high missing data, pollution poses, health risks
类目: Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an)
*备注: Accepted by IEEE CIETES 2025, with 8 pages, 3 figures, and 2 tables

点击查看摘要

Abstract:Urban pollution poses serious health risks, particularly in relation to traffic-related air pollution, which remains a major concern in many cities. Vehicle emissions contribute to respiratory and cardiovascular issues, especially for vulnerable and exposed road users like pedestrians and cyclists. Therefore, accurate air quality monitoring with high spatial resolution is vital for good urban environmental management. This study aims to provide insights for processing spatiotemporal datasets with high missing data rates. In this study, the challenge of high missing data rates is a result of the limited data available and the fine granularity required for precise classification of PM2.5 levels. The data used for analysis and imputation were collected from both mobile sensors and fixed stations by Dynamic Parcel Distribution, the Environmental Protection Agency, and Google in Dublin, Ireland, where the missing data rate was approximately 82.42%, making accurate Particulate Matter 2.5 level predictions particularly difficult. Various imputation and prediction approaches were evaluated and compared, including ensemble methods, deep learning models, and diffusion models. External features such as traffic flow, weather conditions, and data from the nearest stations were incorporated to enhance model performance. The results indicate that diffusion methods with external features achieved the highest F1 score, reaching 0.9486 (Accuracy: 94.26%, Precision: 94.42%, Recall: 94.82%), with ensemble models achieving the highest accuracy of 94.82%, illustrating that good performance can be obtained despite a high missing data rate.

[LG-11] Harvesting energy from turbulent winds with Reinforcement Learning

链接: https://arxiv.org/abs/2412.13961
作者: Lorenzo Basile,Maria Grazia Berni,Antonio Celani
关键词: Airborne Wind Energy, emerging technology designed, conventional wind turbines, Airborne Wind, offering a solution
类目: Machine Learning (cs.LG); Systems and Control (eess.SY); Fluid Dynamics (physics.flu-dyn)
*备注:

点击查看摘要

Abstract:Airborne Wind Energy (AWE) is an emerging technology designed to harness the power of high-altitude winds, offering a solution to several limitations of conventional wind turbines. AWE is based on flying devices (usually gliders or kites) that, tethered to a ground station and driven by the wind, convert its mechanical energy into electrical energy by means of a generator. Such systems are usually controlled by manoeuvering the kite so as to follow a predefined path prescribed by optimal control techniques, such as model-predictive control. These methods are strongly dependent on the specific model at use and difficult to generalize, especially in unpredictable conditions such as the turbulent atmospheric boundary layer. Our aim is to explore the possibility of replacing these techniques with an approach based on Reinforcement Learning (RL). Unlike traditional methods, RL does not require a predefined model, making it robust to variability and uncertainty. Our experimental results in complex simulated environments demonstrate that AWE agents trained with RL can effectively extract energy from turbulent flows, relying on minimal local information about the kite orientation and speed relative to the wind.

[LG-12] Self-attentive Transformer for Fast and Accurate Postprocessing of Temperature and Wind Speed Forecasts

链接: https://arxiv.org/abs/2412.13957
作者: Aaron Van Poecke,Tobias Sebastian Finn,Ruoke Meng,Joris Van den Bergh,Geert Smet,Jonathan Demaeyer,Piet Termonia,Hossein Tabari,Peter Hellinckx
关键词: Current postprocessing techniques, employing distributional approaches, require separate models, Current postprocessing, distributional approaches
类目: Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph)
*备注: 21 pages, 7 figures, submitted to Artificial Intelligence for the Earth Systems (AIES)

点击查看摘要

Abstract:Current postprocessing techniques often require separate models for each lead time and disregard possible inter-ensemble relationships by either correcting each member separately or by employing distributional approaches. In this work, we tackle these shortcomings with an innovative, fast and accurate Transformer which postprocesses each ensemble member individually while allowing information exchange across variables, spatial dimensions and lead times by means of multi-headed self-attention. Weather foreacasts are postprocessed over 20 lead times simultaneously while including up to twelve meteorological predictors. We use the EUPPBench dataset for training which contains ensemble predictions from the European Center for Medium-range Weather Forecasts’ integrated forecasting system alongside corresponding observations. The work presented here is the first to postprocess the ten and one hundred-meter wind speed forecasts within this benchmark dataset, while also correcting the two-meter temperature. Our approach significantly improves the original forecasts, as measured by the CRPS, with 17.5 % for two-meter temperature, nearly 5% for ten-meter wind speed and 5.3 % for one hundred-meter wind speed, outperforming a classical member-by-member approach employed as competitive benchmark. Furthermore, being up to 75 times faster, it fulfills the demand for rapid operational weather forecasts in various downstream applications, including renewable energy forecasting.

[LG-13] hreshold Neuron: A Brain-inspired Artificial Neuron for Efficient On-device Inference

链接: https://arxiv.org/abs/2412.13902
作者: Zihao Zheng,Yuanchun Li,Jiayu Chen,Peng Zhou,Xiang Chen,Yunxin Liu
关键词: on-device Deep Neural, Deep Neural Networks, significant challengein mobile, on-device Deep, Deep Neural
类目: Machine Learning (cs.LG)
*备注: 14 pages, 11 figures

点击查看摘要

Abstract:Enhancing the computational efficiency of on-device Deep Neural Networks (DNNs) remains a significant challengein mobile and edge computing. As we aim to execute increasingly complex tasks with constrained computational resources, much of the research has focused on compressing neural network structures and optimizing systems. Although many studies have focused on compressing neural network structures and parameters or optimizing underlying systems, there has been limited attention on optimizing the fundamental building blocks of neural networks: the neurons. In this study, we deliberate on a simple but important research question: Can we design artificial neurons that offer greater efficiency than the traditional neuron paradigm? Inspired by the threshold mechanisms and the excitation-inhibition balance observed in biological neurons, we propose a novel artificial neuron model, Threshold Neurons. Using Threshold Neurons, we can construct neural networks similar to those with traditional artificial neurons, while significantly reducing hardware implementation complexity. Our extensive experiments validate the effectiveness of neural networks utilizing Threshold Neurons, achieving substantial power savings of 7.51x to 8.19x and area savings of 3.89x to 4.33x at the kernel level, with minimal loss in precision. Furthermore, FPGA-based implementations of these networks demonstrate 2.52x power savings and 1.75x speed enhancements at the system level. The source code will be made available upon publication.

[LG-14] Graph-Driven Models for Gas Mixture Identification and Concentration Estimation on Heterogeneous Sensor Array Signals

链接: https://arxiv.org/abs/2412.13891
作者: Ding Wang,Lei Wang,Huilin Yin,Guoqing Gu,Zhiping Lin,Wenwen Zhang
关键词: Accurately identifying gas, Accurately identifying, gas sensor arrays, sensor arrays, Graph-Enhanced Capsule Network
类目: Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注:

点击查看摘要

Abstract:Accurately identifying gas mixtures and estimating their concentrations are crucial across various industrial applications using gas sensor arrays. However, existing models face challenges in generalizing across heterogeneous datasets, which limits their scalability and practical applicability. To address this problem, this study develops two novel deep-learning models that integrate temporal graph structures for enhanced performance: a Graph-Enhanced Capsule Network (GraphCapsNet) employing dynamic routing for gas mixture classification and a Graph-Enhanced Attention Network (GraphANet) leveraging self-attention for concentration estimation. Both models were validated on datasets from the University of California, Irvine (UCI) Machine Learning Repository and a custom dataset, demonstrating superior performance in gas mixture identification and concentration estimation compared to recent models. In classification tasks, GraphCapsNet achieved over 98.00% accuracy across multiple datasets, while in concentration estimation, GraphANet attained an R2 score exceeding 0.96 across various gas components. Both GraphCapsNet and GraphANet exhibited significantly higher accuracy and stability, positioning them as promising solutions for scalable gas analysis in industrial settings.

[LG-15] Constructing sensible baselines for Integrated Gradients AAAI

链接: https://arxiv.org/abs/2412.13864
作者: Jai Bardhan,Cyrin Neeraj,Mihir Rawat,Subhadip Mitra
关键词: Machine learning methods, Machine learning, scientific community, learning methods, meteoric rise
类目: Machine Learning (cs.LG); High Energy Physics - Experiment (hep-ex)
*备注: 7 pages, 5 figures. Accepted to 4th Annual AAAI Workshop on AI to Accelerate Science and Engineering (AI2ASE)

点击查看摘要

Abstract:Machine learning methods have seen a meteoric rise in their applications in the scientific community. However, little effort has been put into understanding these “black box” models. We show how one can apply integrated gradients (IGs) to understand these models by designing different baselines, by taking an example case study in particle physics. We find that the zero-vector baseline does not provide good feature attributions and that an averaged baseline sampled from the background events provides consistently more reasonable attributions.

[LG-16] RadField3D: A Data Generator and Data Format for Deep Learning in Radiation-Protection Dosimetry for Medical Applications

链接: https://arxiv.org/abs/2412.13852
作者: Felix Lehner,Pasquale Lombardo,Susana Castillo,Oliver Hupe,Marcus Magnor
关键词: Monte-Carlo simulation application, generating threedimensional radiation, threedimensional radiation field, radiation field datasets, present our open-source
类目: Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
*备注:

点击查看摘要

Abstract:In this research work, we present our open-source Geant4-based Monte-Carlo simulation application, called RadField3D, for generating threedimensional radiation field datasets for dosimetry. Accompanying, we introduce a fast, machine-interpretable data format with a Python API for easy integration into neural network research, that we call RadFiled3D. Both developments are intended to be used to research alternative radiation simulation methods using deep learning.

[LG-17] Graph Coarsening via Supervised Granular-Ball for Scalable Graph Neural Network Training

链接: https://arxiv.org/abs/2412.13842
作者: Shuyin Xia,Xinjun Ma,Zhiyuan Liu,Cheng Liu,Sen Zhao,Guoyin Wang
关键词: demonstrated significant achievements, Graph Neural Networks, Neural Networks, Graph Neural, substantial challenge
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Graph Neural Networks (GNNs) have demonstrated significant achievements in processing graph data, yet scalability remains a substantial challenge. To address this, numerous graph coarsening methods have been developed. However, most existing coarsening methods are training-dependent, leading to lower efficiency, and they all require a predefined coarsening rate, lacking an adaptive approach. In this paper, we employ granular-ball computing to effectively compress graph data. We construct a coarsened graph network by iteratively splitting the graph into granular-balls based on a purity threshold and using these granular-balls as super vertices. This granulation process significantly reduces the size of the original graph, thereby greatly enhancing the training efficiency and scalability of GNNs. Additionally, our algorithm can adaptively perform splitting without requiring a predefined coarsening rate. Experimental results demonstrate that our method achieves accuracy comparable to training on the original graph. Noise injection experiments further indicate that our method exhibits robust performance. Moreover, our approach can reduce the graph size by up to 20 times without compromising test accuracy, substantially enhancing the scalability of GNNs.

[LG-18] Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey

链接: https://arxiv.org/abs/2412.13840
作者: Yichen Li,Haozhao Wang,Wenchao Xu,Tianzhe Xiao,Hong Liu,Minzhu Tu,Yuying Wang,Xin Yang,Rui Zhang,Shui Yu,Song Guo,Ruixuan Li
关键词: joint non-stationary environment, handle streaming data, Non-Centralized Continual Learning, enabling distributed devices, Continual Learning
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注:

点击查看摘要

Abstract:Non-Centralized Continual Learning (NCCL) has become an emerging paradigm for enabling distributed devices such as vehicles and servers to handle streaming data from a joint non-stationary environment. To achieve high reliability and scalability in deploying this paradigm in distributed systems, it is essential to conquer challenges stemming from both spatial and temporal dimensions, manifesting as distribution shifts, catastrophic forgetting, heterogeneity, and privacy issues. This survey focuses on a comprehensive examination of the development of the non-centralized continual learning algorithms and the real-world deployment across distributed devices. We begin with an introduction to the background and fundamentals of non-centralized learning and continual learning. Then, we review existing solutions from three levels to represent how existing techniques alleviate the catastrophic forgetting and distribution shift. Additionally, we delve into the various types of heterogeneity issues, security, and privacy attributes, as well as real-world applications across three prevalent scenarios. Furthermore, we establish a large-scale benchmark to revisit this problem and analyze the performance of the state-of-the-art NCCL approaches. Finally, we discuss the important challenges and future research directions in NCCL.

[LG-19] Extreme Multi-label Completion for Semantic Document Labelling with Taxonomy-Aware Parallel Learning

链接: https://arxiv.org/abs/2412.13809
作者: Julien Audiffren,Christophe Broillet,Ljiljana Dolamic,Philippe Cudré-Mauroux
关键词: Multi Label Completion, Extreme Multi Label, Extreme Multi, Multi Label, Extreme multi-label Completion
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:In Extreme Multi Label Completion (XMLCo), the objective is to predict the missing labels of a collection of documents. Together with XML Classification, XMLCo is arguably one of the most challenging document classification tasks, as the very high number of labels (at least ten of thousands) is generally very large compared to the number of available labelled documents in the training dataset. Such a task is often accompanied by a taxonomy that encodes the labels organic relationships, and many methods have been proposed to leverage this hierarchy to improve the results of XMLCo algorithms. In this paper, we propose a new approach to this problem, TAMLEC (Taxonomy-Aware Multi-task Learning for Extreme multi-label Completion). TAMLEC divides the problem into several Taxonomy-Aware Tasks, i.e. subsets of labels adapted to the hierarchical paths of the taxonomy, and trains on these tasks using a dynamic Parallel Feature sharing approach, where some parts of the model are shared between tasks while others are task-specific. Then, at inference time, TAMLEC uses the labels available in a document to infer the appropriate tasks and to predict missing labels. To achieve this result, TAMLEC uses a modified transformer architecture that predicts ordered sequences of labels on a Weak-Semilattice structure that is naturally induced by the tasks. This approach yields multiple advantages. First, our experiments on real-world datasets show that TAMLEC outperforms state-of-the-art methods for various XMLCo problems. Second, TAMLEC is by construction particularly suited for few-shots XML tasks, where new tasks or labels are introduced with only few examples, and extensive evaluations highlight its strong performance compared to existing methods.

[LG-20] oward Efficient Data-Free Unlearning AAAI2025

链接: https://arxiv.org/abs/2412.13790
作者: Chenhao Zhang,Shaofei Shen,Weitong Chen,Miao Xu
关键词: real data distribution, Machine unlearning, distribution is challenging, access to real, real data
类目: Machine Learning (cs.LG)
*备注: 15 pages, 10 figures, accepted by AAAI 2025

点击查看摘要

Abstract:Machine unlearning without access to real data distribution is challenging. The existing method based on data-free distillation achieved unlearning by filtering out synthetic samples containing forgetting information but struggled to distill the retaining-related knowledge efficiently. In this work, we analyze that such a problem is due to over-filtering, which reduces the synthesized retaining-related information. We propose a novel method, Inhibited Synthetic PostFilter (ISPF), to tackle this challenge from two perspectives: First, the Inhibited Synthetic, by reducing the synthesized forgetting information; Second, the PostFilter, by fully utilizing the retaining-related information in synthesized samples. Experimental results demonstrate that the proposed ISPF effectively tackles the challenge and outperforms existing methods.

[LG-21] Rehearsal-Free Continual Federated Learning with Synergistic Regularization

链接: https://arxiv.org/abs/2412.13779
作者: Yichen Li,Yuying Wang,Tianzhe Xiao,Haozhao Wang,Yining Qi,Ruixuan Li
关键词: Continual Federated Learning, Continual Federated, Federated Learning, continuously shifting training, avoiding knowledge forgetting
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注:

点击查看摘要

Abstract:Continual Federated Learning (CFL) allows distributed devices to collaboratively learn novel concepts from continuously shifting training data while avoiding knowledge forgetting of previously seen tasks. To tackle this challenge, most current CFL approaches rely on extensive rehearsal of previous data. Despite effectiveness, rehearsal comes at a cost to memory, and it may also violate data privacy. Considering these, we seek to apply regularization techniques to CFL by considering their cost-efficient properties that do not require sample caching or rehearsal. Specifically, we first apply traditional regularization techniques to CFL and observe that existing regularization techniques, especially synaptic intelligence, can achieve promising results under homogeneous data distribution but fail when the data is heterogeneous. Based on this observation, we propose a simple yet effective regularization algorithm for CFL named FedSSI, which tailors the synaptic intelligence for the CFL with heterogeneous data settings. FedSSI can not only reduce computational overhead without rehearsal but also address the data heterogeneity issue. Extensive experiments show that FedSSI achieves superior performance compared to state-of-the-art methods.

[LG-22] Cultivating Archipelago of Forests: Evolving Robust Decision Trees through Island Coevolution

链接: https://arxiv.org/abs/2412.13762
作者: Adam Żychowski,Andrew Perrault,Jacek Mańdziuk
关键词: Decision trees, simplicity and interpretability, decision tree ensembles, lack robustness, attacks and data
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
*备注:

点击查看摘要

Abstract:Decision trees are widely used in machine learning due to their simplicity and interpretability, but they often lack robustness to adversarial attacks and data perturbations. The paper proposes a novel island-based coevolutionary algorithm (ICoEvoRDF) for constructing robust decision tree ensembles. The algorithm operates on multiple islands, each containing populations of decision trees and adversarial perturbations. The populations on each island evolve independently, with periodic migration of top-performing decision trees between islands. This approach fosters diversity and enhances the exploration of the solution space, leading to more robust and accurate decision tree ensembles. ICoEvoRDF utilizes a popular game theory concept of mixed Nash equilibrium for ensemble weighting, which further leads to improvement in results. ICoEvoRDF is evaluated on 20 benchmark datasets, demonstrating its superior performance compared to state-of-the-art methods in optimizing both adversarial accuracy and minimax regret. The flexibility of ICoEvoRDF allows for the integration of decision trees from various existing methods, providing a unified framework for combining diverse solutions. Our approach offers a promising direction for developing robust and interpretable machine learning models

[LG-23] Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Data WACV2025

链接: https://arxiv.org/abs/2412.13757
作者: Junki Mori,Kosuke Kihara,Taiki Miyagawa,Akinori F. Ebihara,Isamu Teranishi,Hisashi Kashima
关键词: source-free domain adaptation, commonly assumes, domain adaptation, Federated source-Free Domain, impractical due
类目: Machine Learning (cs.LG)
*备注: Accepted by WACV 2025

点击查看摘要

Abstract:Federated learning (FL) commonly assumes that the server or some clients have labeled data, which is often impractical due to annotation costs and privacy concerns. Addressing this problem, we focus on a source-free domain adaptation task, where (1) the server holds a pre-trained model on labeled source domain data, (2) clients possess only unlabeled data from various target domains, and (3) the server and clients cannot access the source data in the adaptation phase. This task is known as Federated source-Free Domain Adaptation (FFREEDA). Specifically, we focus on classification tasks, while the previous work solely studies semantic segmentation. Our contribution is the novel Federated learning with Weighted Cluster Aggregation (FedWCA) method, designed to mitigate both domain shifts and privacy concerns with only unlabeled data. FedWCA comprises three phases: private and parameter-free clustering of clients to obtain domain-specific global models on the server, weighted aggregation of the global models for the clustered clients, and local domain adaptation with pseudo-labeling. Experimental results show that FedWCA surpasses several existing methods and baselines in FFREEDA, establishing its effectiveness and practicality.

[LG-24] Optimal Exact Recovery in Semi-Supervised Learning: A Study of Spectral Methods and Graph Convolutional Networks ICML2024

链接: https://arxiv.org/abs/2412.13754
作者: Hai-Xiao Wang,Zhichao Wang
关键词: Stochastic Block Model, Contextual Stochastic Block, Gaussian Mixture Model, Block Model, two-cluster Stochastic Block
类目: Machine Learning (cs.LG); Probability (math.PR); Statistics Theory (math.ST); Machine Learning (stat.ML)
*备注: Accepted by ICML 2024. The conference version can be accessed via this https URL

点击查看摘要

Abstract:We delve into the challenge of semi-supervised node classification on the Contextual Stochastic Block Model (CSBM) dataset. Here, nodes from the two-cluster Stochastic Block Model (SBM) are coupled with feature vectors, which are derived from a Gaussian Mixture Model (GMM) that corresponds to their respective node labels. With only a subset of the CSBM node labels accessible for training, our primary objective becomes the accurate classification of the remaining nodes. Venturing into the transductive learning landscape, we, for the first time, pinpoint the information-theoretical threshold for the exact recovery of all test nodes in CSBM. Concurrently, we design an optimal spectral estimator inspired by Principal Component Analysis (PCA) with the training labels and essential data from both the adjacency matrix and feature vectors. We also evaluate the efficacy of graph ridge regression and Graph Convolutional Networks (GCN) on this synthetic dataset. Our findings underscore that graph ridge regression and GCN possess the ability to achieve the information threshold of exact recovery in a manner akin to the optimal estimator when using the optimal weighted self-loops. This highlights the potential role of feature learning in augmenting the proficiency of GCN, especially in the realm of semi-supervised learning.

[LG-25] H"OR-MAGNI Act: Actions for Human Motion Modeling in Robot-Shared Industrial Spaces

链接: https://arxiv.org/abs/2412.13729
作者: Tiago Rodrigues de Almeida,Tim Schreiter,Andrey Rudenko,Luigi Palmieiri,Johannes A. Stork,Achim J. Lilienthal
关键词: Accurate human activity, Accurate human, THÖR-MAGNI Act, human activity, crucial for ensuring
类目: Robotics (cs.RO); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
*备注: This paper has been accepted to the the 20th edition of the IEEE/ACM International Conference on Human-Robot Interaction (HRI’25), which will be held in Melbourne, Australia on March 4-6, 2025. Code: this https URL

点击查看摘要

Abstract:Accurate human activity and trajectory prediction are crucial for ensuring safe and reliable human-robot interactions in dynamic environments, such as industrial settings, with mobile robots. Datasets with fine-grained action labels for moving people in industrial environments with mobile robots are scarce, as most existing datasets focus on social navigation in public spaces. This paper introduces the THÖR-MAGNI Act dataset, a substantial extension of the THÖR-MAGNI dataset, which captures participant movements alongside robots in diverse semantic and spatial contexts. THÖR-MAGNI Act provides 8.3 hours of manually labeled participant actions derived from egocentric videos recorded via eye-tracking glasses. These actions, aligned with the provided THÖR-MAGNI motion cues, follow a long-tailed distribution with diversified acceleration, velocity, and navigation distance profiles. We demonstrate the utility of THÖR-MAGNI Act for two tasks: action-conditioned trajectory prediction and joint action and trajectory prediction. We propose two efficient transformer-based models that outperform the baselines to address these tasks. These results underscore the potential of THÖR-MAGNI Act to develop predictive models for enhanced human-robot interaction in complex environments.

[LG-26] USEFUSE: Utile Stride for Enhanced Performance in Fused Layer Architecture of Deep Neural Networks

链接: https://arxiv.org/abs/2412.13724
作者: Muhammad Sohail Ibrahim,Muhammad Usman,Jeong-A Lee
关键词: Convolutional Neural Networks, Convolutional Neural, Neural Networks, devices poses challenges, poses challenges
类目: Machine Learning (cs.LG); Hardware Architecture (cs.AR); Performance (cs.PF)
*备注:

点击查看摘要

Abstract:Convolutional Neural Networks (CNNs) are crucial in various applications, but their deployment on resource-constrained edge devices poses challenges. This study presents the Sum-of-Products (SOP) units for convolution, which utilize low-latency left-to-right bit-serial arithmetic to minimize response time and enhance overall performance. The study proposes a methodology for fusing multiple convolution layers to reduce off-chip memory communication and increase overall performance. An effective mechanism detects and skips inefficient convolutions after ReLU layers, minimizing power consumption without compromising accuracy. Furthermore, efficient tile movement guarantees uniform access to the fusion pyramid. An analysis demonstrates the utile stride strategy improves operational intensity. Two designs cater to varied demands: one focuses on minimal response time for mission-critical applications, and another focuses on resource-constrained devices with comparable latency. This approach notably reduced redundant computations, improving the efficiency of CNN deployment on edge devices.

[LG-27] SSE-SAM: Balancing Head and Tail Classes Gradually through Stage-Wise SAM

链接: https://arxiv.org/abs/2412.13715
作者: Xingyu Lyu,Qianqian Xu,Zhiyong Yang,Shaojie Lyu,Qingming Huang
关键词: Real-world datasets, tail classes, SAM, classes, datasets often exhibit
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Real-world datasets often exhibit a long-tailed distribution, where vast majority of classes known as tail classes have only few samples. Traditional methods tend to overfit on these tail classes. Recently, a new approach called Imbalanced SAM (ImbSAM) is proposed to leverage the generalization benefits of Sharpness-Aware Minimization (SAM) for long-tailed distributions. The main strategy is to merely enhance the smoothness of the loss function for tail classes. However, we argue that improving generalization in long-tail scenarios requires a careful balance between head and tail classes. We show that neither SAM nor ImbSAM alone can fully achieve this balance. For SAM, we prove that although it enhances the model’s generalization ability by escaping saddle point in the overall loss landscape, it does not effectively address this for tail-class losses. Conversely, while ImbSAM is more effective at avoiding saddle points in tail classes, the head classes are trained insufficiently, resulting in significant performance drops. Based on these insights, we propose Stage-wise Saddle Escaping SAM (SSE-SAM), which uses complementary strengths of ImbSAM and SAM in a phased approach. Initially, SSE-SAM follows the majority sample to avoid saddle points of the head-class loss. During the later phase, it focuses on tail-classes to help them escape saddle points. Our experiments confirm that SSE-SAM has better ability in escaping saddles both on head and tail classes, and shows performance improvements.

[LG-28] AnchorInv: Few-Shot Class-Incremental Learning of Physiological Signals via Representation Space Guided Inversion AAAI-25

链接: https://arxiv.org/abs/2412.13714
作者: Chenqi Li,Boyan Gao,Gabriel Jones,Timothy Denison,Tingting Zhu
关键词: demonstrated exceptional performance, Deep learning models, Deep learning, demonstrated exceptional, exceptional performance
类目: Machine Learning (cs.LG)
*备注: AAAI-25 Extended Version

点击查看摘要

Abstract:Deep learning models have demonstrated exceptional performance in a variety of real-world applications. These successes are often attributed to strong base models that can generalize to novel tasks with limited supporting data while keeping prior knowledge intact. However, these impressive results are based on the availability of a large amount of high-quality data, which is often lacking in specialized biomedical applications. In such fields, models are usually developed with limited data that arrive incrementally with novel categories. This requires the model to adapt to new information while preserving existing knowledge. Few-Shot Class-Incremental Learning (FSCIL) methods offer a promising approach to addressing these challenges, but they also depend on strong base models that face the same aforementioned limitations. To overcome these constraints, we propose AnchorInv following the straightforward and efficient buffer-replay strategy. Instead of selecting and storing raw data, AnchorInv generates synthetic samples guided by anchor points in the feature space. This approach protects privacy and regularizes the model for adaptation. When evaluated on three public physiological time series datasets, AnchorInv exhibits efficient knowledge forgetting prevention and improved adaptation to novel classes, surpassing state-of-the-art baselines.

[LG-29] Splitting criteria for ordinal decision trees: an experimental study

链接: https://arxiv.org/abs/2412.13697
作者: Rafael Ayllón-Gavilán,Francisco José Martínez-Estudillo,David Guijo-Rubio,César Hervás-Martínez,Pedro Antonio Gutiérrez
关键词: machine learning field, addresses classification tasks, natural order, Ordinal, machine learning
类目: Machine Learning (cs.LG)
*备注: 11 pages, 3 figures, 6 tables

点击查看摘要

Abstract:Ordinal Classification (OC) is a machine learning field that addresses classification tasks where the labels exhibit a natural order. Unlike nominal classification, which treats all classes as equally distinct, OC takes the ordinal relationship into account, producing more accurate and relevant results. This is particularly critical in applications where the magnitude of classification errors has implications. Despite this, OC problems are often tackled using nominal methods, leading to suboptimal solutions. Although decision trees are one of the most popular classification approaches, ordinal tree-based approaches have received less attention when compared to other classifiers. This work conducts an experimental study of tree-based methodologies specifically designed to capture ordinal relationships. A comprehensive survey of ordinal splitting criteria is provided, standardising the notations used in the literature for clarity. Three ordinal splitting criteria, Ordinal Gini (OGini), Weighted Information Gain (WIG), and Ranking Impurity (RI), are compared to the nominal counterparts of the first two (Gini and information gain), by incorporating them into a decision tree classifier. An extensive repository considering 45 publicly available OC datasets is presented, supporting the first experimental comparison of ordinal and nominal splitting criteria using well-known OC evaluation metrics. Statistical analysis of the results highlights OGini as the most effective ordinal splitting criterion to date. Source code, datasets, and results are made available to the research community.

[LG-30] Personalized Clustering via Targeted Representation Learning AAAI2025

链接: https://arxiv.org/abs/2412.13690
作者: Xiwen Geng,Suyun Zhao,Yixin Yu,Borui Peng,Pan Du,Hong Chen,Cuiping Li,Mengdie Wang
关键词: natural grouping structure, grouping structure model, Clustering traditionally aims, unlabeled data, traditionally aims
类目: Machine Learning (cs.LG)
*备注: Accepted to AAAI 2025 main conference

点击查看摘要

Abstract:Clustering traditionally aims to reveal a natural grouping structure model from unlabeled data. However, this model may not always align with users’ preference. In this paper, we propose a personalized clustering method that explicitly performs targeted representation learning by interacting with users via modicum task information (e.g., \textitmust-link or \textitcannot-link pairs) to guide the clustering direction. We query users with the most informative pairs, i.e., those pairs most hard to cluster and those most easy to miscluster, to facilitate the representation learning in terms of the clustering preference. Moreover, by exploiting attention mechanism, the targeted representation is learned and augmented. By leveraging the targeted representation and constrained constrastive loss as well, personalized clustering is obtained. Theoretically, we verify that the risk of personalized clustering is tightly bounded, guaranteeing that active queries to users do mitigate the clustering risk. Experimentally, extensive results show that our method performs well across different clustering tasks and datasets, even with a limited number of queries.

[LG-31] On Enhancing Root Cause Analysis with SQL Summaries for Failures in Database Workload Replays at SAP HANA

链接: https://arxiv.org/abs/2412.13679
作者: Neetha Jambigi,Joshua Hammesfahr,Moritz Mueller,Thomas Bach,Michael Felderer
关键词: Capturing the workload, regression testing, database and replaying, replaying this workload, Capturing
类目: Machine Learning (cs.LG); Databases (cs.DB)
*备注: The 35th IEEE International Symposium on Software Reliability Engineering

点击查看摘要

Abstract:Capturing the workload of a database and replaying this workload for a new version of the database can be an effective approach for regression testing. However, false positive errors caused by many factors such as data privacy limitations, time dependency or non-determinism in multi-threaded environment can negatively impact the effectiveness. Therefore, we employ a machine learning based framework to automate the root cause analysis of failures found during replays. However, handling unseen novel issues not found in the training data is one general challenge of machine learning approaches with respect to generalizability of the learned model. We describe how we continue to address this challenge for more robust long-term solutions. From our experience, retraining with new failures is inadequate due to features overlapping across distinct root causes. Hence, we leverage a large language model (LLM) to analyze failed SQL statements and extract concise failure summaries as an additional feature to enhance the classification process. Our experiments show the F1-Macro score improved by 4.77% for our data. We consider our approach beneficial for providing end users with additional information to gain more insights into the found issues and to improve the assessment of the replay results.

[LG-32] AUDiff: Improving statistical downscaling for extreme weather events using generative diffusion models

链接: https://arxiv.org/abs/2412.13627
作者: Rahul Sundar,Nishant Parashar,Antoine Blanchard,Boyko Dodov
关键词: Deterministic regression-based downscaling, climate variables, variables often suffer, regression-based downscaling models, spectral bias
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Deterministic regression-based downscaling models for climate variables often suffer from spectral bias, which can be mitigated by generative models like diffusion models. To enable efficient and reliable simulation of extreme weather events, it is crucial to achieve rapid turnaround, dynamical consistency, and accurate spatio-temporal spectral recovery. We propose an efficient correction diffusion model, TAUDiff, that combines a deterministic spatio-temporal model for mean field downscaling with a smaller generative diffusion model for recovering the fine-scale stochastic features. We demonstrate the efficacy of this approach on downscaling atmospheric wind velocity fields obtained from coarse GCM simulations. Our approach can not only ensure quicker simulation of extreme events but also reduce overall carbon footprint due to low inference times.

[LG-33] PreMixer: MLP-Based Pre-training Enhanced MLP-Mixers for Large-scale Traffic Forecasting

链接: https://arxiv.org/abs/2412.13607
作者: Tongtong Zhang,Zhiyong Cui,Bingzhang Wang,Yilong Ren,Haiyang Yu,Pan Deng,Yinhai Wang
关键词: precise and swift, urban computing, traffic, multivariate time series, road network layouts
类目: Machine Learning (cs.LG); Emerging Technologies (cs.ET)
*备注:

点击查看摘要

Abstract:In urban computing, precise and swift forecasting of multivariate time series data from traffic networks is crucial. This data incorporates additional spatial contexts such as sensor placements and road network layouts, and exhibits complex temporal patterns that amplify challenges for predictive learning in traffic management, smart mobility demand, and urban planning. Consequently, there is an increasing need to forecast traffic flow across broader geographic regions and for higher temporal coverage. However, current research encounters limitations because of the inherent inefficiency of model and their unsuitability for large-scale traffic network applications due to model complexity. This paper proposes a novel framework, named PreMixer, designed to bridge this gap. It features a predictive model and a pre-training mechanism, both based on the principles of Multi-Layer Perceptrons (MLP). The PreMixer comprehensively consider temporal dependencies of traffic patterns in different time windows and processes the spatial dynamics as well. Additionally, we integrate spatio-temporal positional encoding to manage spatiotemporal heterogeneity without relying on predefined graphs. Furthermore, our innovative pre-training model uses a simple patch-wise MLP to conduct masked time series modeling, learning from long-term historical data segmented into patches to generate enriched contextual representations. This approach enhances the downstream forecasting model without incurring significant time consumption or computational resource demands owing to improved learning efficiency and data handling flexibility. Our framework achieves comparable state-of-the-art performance while maintaining high computational efficiency, as verified by extensive experiments on large-scale traffic datasets.

[LG-34] PASCO (PArallel Structured COarsening): an overlay to speed up graph clustering algorithms

链接: https://arxiv.org/abs/2412.13592
作者: Etienne Lasalle(OCKHAM),Rémi Vaudaine(OCKHAM),Titouan Vayer(OCKHAM),Pierre Borgnat(Phys-ENS),Rémi Gribonval(OCKHAM),Paulo Gonçalves(OCKHAM),Màrton Karsai(CEU)
关键词: extensively studied, graph, Clustering, spectral clustering requires, Laplacian matrix
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:

点击查看摘要

Abstract:Clustering the nodes of a graph is a cornerstone of graph analysis and has been extensively studied. However, some popular methods are not suitable for very large graphs: e.g., spectral clustering requires the computation of the spectral decomposition of the Laplacian matrix, which is not applicable for large graphs with a large number of communities. This work introduces PASCO, an overlay that accelerates clustering algorithms. Our method consists of three steps: 1-We compute several independent small graphs representing the input graph by applying an efficient and structure-preserving coarsening algorithm. 2-A clustering algorithm is run in parallel onto each small graph and provides several partitions of the initial graph. 3-These partitions are aligned and combined with an optimal transport method to output the final partition. The PASCO framework is based on two key contributions: a novel global algorithm structure designed to enable parallelization and a fast, empirically validated graph coarsening algorithm that preserves structural properties. We demonstrate the strong performance of 1 PASCO in terms of computational efficiency, structural preservation, and output partition quality, evaluated on both synthetic and real-world graph datasets.

[LG-35] PowerMLP: An Efficient Version of KAN

链接: https://arxiv.org/abs/2412.13571
作者: Ruichen Qiu,Yibo Miao,Shiwen Wang,Lijia Yu,Yifan Zhu,Xiao-Shan Gao
关键词: PDE solving, fitting and PDE, KAN, spline functions, function fitting
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注:

点击查看摘要

Abstract:The Kolmogorov-Arnold Network (KAN) is a new network architecture known for its high accuracy in several tasks such as function fitting and PDE solving. The superior expressive capability of KAN arises from the Kolmogorov-Arnold representation theorem and learnable spline functions. However, the computation of spline functions involves multiple iterations, which renders KAN significantly slower than MLP, thereby increasing the cost associated with model training and deployment. The authors of KAN have also noted that ``the biggest bottleneck of KANs lies in its slow training. KANs are usually 10x slower than MLPs, given the same number of parameters.‘’ To address this issue, we propose a novel MLP-type neural network PowerMLP that employs simpler non-iterative spline function representation, offering approximately the same training time as MLP while theoretically demonstrating stronger expressive power than KAN. Furthermore, we compare the FLOPs of KAN and PowerMLP, quantifying the faster computation speed of PowerMLP. Our comprehensive experiments demonstrate that PowerMLP generally achieves higher accuracy and a training speed about 40 times faster than KAN in various tasks.

[LG-36] Indirect Query Bayesian Optimization with Integrated Feedback

链接: https://arxiv.org/abs/2412.13559
作者: Mengyan Zhang,Shahine Bouabid,Cheng Soon Ong,Seth Flaxman,Dino Sejdinovic
关键词: Indirect Query Bayesian, Query Bayesian Optimization, Bayesian optimization problems, Query Bayesian, Indirect Query
类目: Machine Learning (cs.LG)
*备注: Preliminary work. Under review

点击查看摘要

Abstract:We develop the framework of Indirect Query Bayesian Optimization (IQBO), a new class of Bayesian optimization problems where the integrated feedback is given via a conditional expectation of the unknown function f to be optimized. The underlying conditional distribution can be unknown and learned from data. The goal is to find the global optimum of f by adaptively querying and observing in the space transformed by the conditional distribution. This is motivated by real-world applications where one cannot access direct feedback due to privacy, hardware or computational constraints. We propose the Conditional Max-Value Entropy Search (CMES) acquisition function to address this novel setting, and propose a hierarchical search algorithm to address the multi-resolution setting and improve the computational efficiency. We show regret bounds for our proposed methods and demonstrate the effectiveness of our approaches on simulated optimization tasks.

[LG-37] Multi-view Granular-ball Contrastive Clustering AAAI2025

链接: https://arxiv.org/abs/2412.13550
作者: Peng Su,Shudong Huang,Weihong Ma,Deng Xiong,Jiancheng Lv
关键词: Previous multi-view contrastive, Previous multi-view, methods typically operate, typically operate, Previous
类目: Machine Learning (cs.LG)
*备注: 9 pages, 5 figures, 2 tables, AAAI 2025

点击查看摘要

Abstract:Previous multi-view contrastive learning methods typically operate at two scales: instance-level and cluster-level. Instance-level approaches construct positive and negative pairs based on sample correspondences, aiming to bring positive pairs closer and push negative pairs further apart in the latent space. Cluster-level methods focus on calculating cluster assignments for samples under each view and maximize view consensus by reducing distribution discrepancies, e.g., minimizing KL divergence or maximizing mutual information. However, these two types of methods either introduce false negatives, leading to reduced model discriminability, or overlook local structures and cannot measure relationships between clusters across views explicitly. To this end, we propose a method named Multi-view Granular-ball Contrastive Clustering (MGBCC). MGBCC segments the sample set into coarse-grained granular balls, and establishes associations between intra-view and cross-view granular balls. These associations are reinforced in a shared latent space, thereby achieving multi-granularity contrastive learning. Granular balls lie between instances and clusters, naturally preserving the local topological structure of the sample set. We conduct extensive experiments to validate the effectiveness of the proposed method.

[LG-38] Quantum Machine Learning in Log-based Anomaly Detection: Challenges and Opportunities

链接: https://arxiv.org/abs/2412.13529
作者: Jiaxing Qi,Chang Zeng,Zhongzhi Luan,Shaohan Huang,Shu Yang,Yao Lu,Bin Han,Hailong Yang,Depei Qian
关键词: Log-based anomaly detection, Artificial Intelligence, component of Artificial, Log-based anomaly, main component
类目: Machine Learning (cs.LG); Quantum Physics (quant-ph)
*备注:

点击查看摘要

Abstract:Log-based anomaly detection (LogAD) is the main component of Artificial Intelligence for IT Operations (AIOps), which can detect anomalous that occur during the system on-the-fly. Existing methods commonly extract log sequence features using classical machine learning techniques to identify whether a new sequence is an anomaly or not. However, these classical approaches often require trade-offs between efficiency and accuracy. The advent of quantum machine learning (QML) offers a promising alternative. By transforming parts of classical machine learning computations into parameterized quantum circuits (PQCs), QML can significantly reduce the number of trainable parameters while maintaining accuracy comparable to classical counterparts. In this work, we introduce a unified framework, \ourframework, for evaluating QML models in the context of LogAD. This framework incorporates diverse log data, integrated QML models, and comprehensive evaluation metrics. State-of-the-art methods such as DeepLog, LogAnomaly, and LogRobust, along with their quantum-transformed counterparts, are included in our this http URL standard metrics like F1 score, precision, and recall, our evaluation extends to factors critical to QML performance, such as specificity, the number of circuits, circuit design, and quantum state encoding. Using \ourframework, we conduct extensive experiments to assess the performance of these models and their quantum counterparts, uncovering valuable insights and paving the way for future research in QML model selection and design for LogAD.

[LG-39] Rethink the Evaluation Protocol of Model Merging on Classification Task

链接: https://arxiv.org/abs/2412.13526
作者: Fanshuang Kong,Richong Zhang,Zhijie Nie,Ziqiao Wang
关键词: multiple fine-tuned models, Model merging combines, combines multiple fine-tuned, merging combines multiple, fine-tuned models
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Model merging combines multiple fine-tuned models into a single one via parameter fusion, achieving improvements across many tasks. However, in the classification task, we find a misalignment issue between merging outputs and the fine-tuned classifier, which limits its effectiveness. In this paper, we demonstrate the following observations: (1) The embedding quality of the merging outputs is already very high, and the primary reason for the differences in classification performance lies in the misalignment issue. (2) We propose FT-Classifier, a new protocol that fine-tunes an aligned classifier with few-shot samples to alleviate misalignment, enabling better evaluation of merging outputs and improved classification performance. (3) The misalignment is relatively straightforward and can be formulated as an orthogonal transformation. Experiments demonstrate the existence of misalignment and the effectiveness of our FT-Classifier evaluation protocol.

[LG-40] Open-Source Protein Language Models for Function Prediction and Protein Design AAAI

链接: https://arxiv.org/abs/2412.13519
作者: Shivasankaran Vanaja Pandi,Bharath Ramsundar
关键词: contributing to advances, shown promise, promise in improving, improving the understanding, advances in areas
类目: Machine Learning (cs.LG); Biomolecules (q-bio.BM)
*备注: To be published in 4th Annual AAAI workshop on AI to Accelerate Science and Engineering

点击查看摘要

Abstract:Protein language models (PLMs) have shown promise in improving the understanding of protein sequences, contributing to advances in areas such as function prediction and protein engineering. However, training these models from scratch requires significant computational resources, limiting their accessibility. To address this, we integrate a PLM into DeepChem, an open-source framework for computational biology and chemistry, to provide a more accessible platform for protein-related tasks. We evaluate the performance of the integrated model on various protein prediction tasks, showing that it achieves reasonable results across benchmarks. Additionally, we present an exploration of generating plastic-degrading enzyme candidates using the model’s embeddings and latent space manipulation techniques. While the results suggest that further refinement is needed, this approach provides a foundation for future work in enzyme design. This study aims to facilitate the use of PLMs in research fields like synthetic biology and environmental sustainability, even for those with limited computational resources. Comments: To be published in 4th Annual AAAI workshop on AI to Accelerate Science and Engineering Subjects: Machine Learning (cs.LG); Biomolecules (q-bio.BM) Cite as: arXiv:2412.13519 [cs.LG] (or arXiv:2412.13519v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2412.13519 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

[LG-41] Learning Causal Transition Matrix for Instance-dependent Label Noise

链接: https://arxiv.org/abs/2412.13516
作者: Jiahui Li,Tai-Wei Chang,Kun Kuang,Ximing Li,Long Chen,Jun Zhou
关键词: negatively impact models’, impact models’ generalization, models’ generalization ability, transition matrix, machine learning methods
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Noisy labels are both inevitable and problematic in machine learning methods, as they negatively impact models’ generalization ability by causing overfitting. In the context of learning with noise, the transition matrix plays a crucial role in the design of statistically consistent algorithms. However, the transition matrix is often considered unidentifiable. One strand of methods typically addresses this problem by assuming that the transition matrix is instance-independent; that is, the probability of mislabeling a particular instance is not influenced by its characteristics or attributes. This assumption is clearly invalid in complex real-world scenarios. To better understand the transition relationship and relax this assumption, we propose to study the data generation process of noisy labels from a causal perspective. We discover that an unobservable latent variable can affect either the instance itself, the label annotation procedure, or both, which complicates the identification of the transition matrix. To address various scenarios, we have unified these observations within a new causal graph. In this graph, the input instance is divided into a noise-resistant component and a noise-sensitive component based on whether they are affected by the latent variable. These two components contribute to identifying the ``causal transition matrix’', which approximates the true transition matrix with theoretical guarantee. In line with this, we have designed a novel training framework that explicitly models this causal relationship and, as a result, achieves a more accurate model for inferring the clean label.

[LG-42] Efficient Language-instructed Skill Acquisition via Reward-Policy Co-Evolution AAAI2025

链接: https://arxiv.org/abs/2412.13492
作者: Changxin Huang,Yanbin Chang,Junfan Lin,Junyang Liang,Runhao Zeng,Jianqiang Li
关键词: minimal human guidance, reward function, reward, embodied intelligence, ability to autonomously
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: 14 pages, 5 figures, published to AAAI2025

点击查看摘要

Abstract:The ability to autonomously explore and resolve tasks with minimal human guidance is crucial for the self-development of embodied intelligence. Although reinforcement learning methods can largely ease human effort, it’s challenging to design reward functions for real-world tasks, especially for high-dimensional robotic control, due to complex relationships among joints and tasks. Recent advancements large language models (LLMs) enable automatic reward function design. However, approaches evaluate reward functions by re-training policies from scratch placing an undue burden on the reward function, expecting it to be effective throughout the whole policy improvement process. We argue for a more practical strategy in robotic autonomy, focusing on refining existing policies with policy-dependent reward functions rather than a universal one. To this end, we propose a novel reward-policy co-evolution framework where the reward function and the learned policy benefit from each other’s progressive on-the-fly improvements, resulting in more efficient and higher-performing skill acquisition. Specifically, the reward evolution process translates the robot’s previous best reward function, descriptions of tasks and environment into text inputs. These inputs are used to query LLMs to generate a dynamic amount of reward function candidates, ensuring continuous improvement at each round of evolution. For policy evolution, our method generates new policy populations by hybridizing historically optimal and random policies. Through an improved Bayesian optimization, our approach efficiently and robustly identifies the most capable and plastic reward-policy combination, which then proceeds to the next round of co-evolution. Despite using less data, our approach demonstrates an average normalized improvement of 95.3% across various high-dimensional robotic skill learning tasks.

[LG-43] Efficient Fine-Tuning of Single-Cell Foundation Models Enables Zero-Shot Molecular Perturbation Prediction

链接: https://arxiv.org/abs/2412.13478
作者: Sepideh Maleki,Jan-Christian Huetter,Kangway V. Chuang,Gabriele Scalia,Tommaso Biancalani
关键词: Predicting transcriptional responses, accelerate biomedical research, drug discovery efforts, Predicting transcriptional, advance drug discovery
类目: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
*备注:

点击查看摘要

Abstract:Predicting transcriptional responses to novel drugs provides a unique opportunity to accelerate biomedical research and advance drug discovery efforts. However, the inherent complexity and high dimensionality of cellular responses, combined with the extremely limited available experimental data, makes the task challenging. In this study, we leverage single-cell foundation models (FMs) pre-trained on tens of millions of single cells, encompassing multiple cell types, states, and disease annotations, to address molecular perturbation prediction. We introduce a drug-conditional adapter that allows efficient fine-tuning by training less than 1% of the original foundation model, thus enabling molecular conditioning while preserving the rich biological representation learned during pre-training. The proposed strategy allows not only the prediction of cellular responses to novel drugs, but also the zero-shot generalization to unseen cell lines. We establish a robust evaluation framework to assess model performance across different generalization tasks, demonstrating state-of-the-art results across all settings, with significant improvements in the few-shot and zero-shot generalization to new cell lines compared to existing baselines.

[LG-44] SocialED: A Python Library for Social Event Detection

链接: https://arxiv.org/abs/2412.13472
作者: Kun Zhang,Xiaoyan Yu,Pu Li,Hao Peng,Philip S. Yu
关键词: open-source Python library, Python library designed, open-source Python, social event detection, diverse datasets
类目: Machine Learning (cs.LG); Digital Libraries (cs.DL); Social and Information Networks (cs.SI)
*备注: 8 pages, 1 figure, Python library

点击查看摘要

Abstract:SocialED is a comprehensive, open-source Python library designed to support social event detection (SED) tasks, integrating 19 detection algorithms and 14 diverse datasets. It provides a unified API with detailed documentation, offering researchers and practitioners a complete solution for event detection in social media. The library is designed with modularity in mind, allowing users to easily adapt and extend components for various use cases. SocialED supports a wide range of preprocessing techniques, such as graph construction and tokenization, and includes standardized interfaces for training models and making predictions. By integrating popular deep learning frameworks, SocialED ensures high efficiency and scalability across both CPU and GPU environments. The library is built adhering to high code quality standards, including unit testing, continuous integration, and code coverage, ensuring that SocialED delivers robust, maintainable software. SocialED is publicly available at \urlthis https URL and can be installed via PyPI.

[LG-45] Federated Unlearning Model Recovery in Data with Skewed Label Distributions

链接: https://arxiv.org/abs/2412.13466
作者: Xinrui Yu,Wenbin Pei,Bing Xue,Qiang Zhang
关键词: skewed label distributions, rollback mechanism, skewed, skewed label, unlearning
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:In federated learning, federated unlearning is a technique that provides clients with a rollback mechanism that allows them to withdraw their data contribution without training from scratch. However, existing research has not considered scenarios with skewed label distributions. Unfortunately, the unlearning of a client with skewed data usually results in biased models and makes it difficult to deliver high-quality service, complicating the recovery process. This paper proposes a recovery method of federated unlearning with skewed label distributions. Specifically, we first adopt a strategy that incorporates oversampling with deep learning to supplement the skewed class data for clients to perform recovery training, therefore enhancing the completeness of their local datasets. Afterward, a density-based denoising method is applied to remove noise from the generated data, further improving the quality of the remaining clients’ datasets. Finally, all the remaining clients leverage the enhanced local datasets and engage in iterative training to effectively restore the performance of the unlearning model. Extensive evaluations on commonly used federated learning datasets with varying degrees of skewness show that our method outperforms baseline methods in restoring the performance of the unlearning model, particularly regarding accuracy on the skewed class.

[LG-46] Rare Event Detection in Imbalanced Multi-Class Datasets Using an Optimal MIP-Based Ensemble Weighting Approach AAAI AAAI25

链接: https://arxiv.org/abs/2412.13439
作者: Georgios Tertytchny,Georgios L. Stavrinides,Maria K. Michael
关键词: critical cyber-physical systems, mixed integer programming, rare event detection, adaptable mixed integer, imbalanced multi-class datasets
类目: Machine Learning (cs.LG)
*备注: To be published in the Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI25)

点击查看摘要

Abstract:To address the challenges of imbalanced multi-class datasets typically used for rare event detection in critical cyber-physical systems, we propose an optimal, efficient, and adaptable mixed integer programming (MIP) ensemble weighting scheme. Our approach leverages the diverse capabilities of the classifier ensemble on a granular per class basis, while optimizing the weights of classifier-class pairs using elastic net regularization for improved robustness and generalization. Additionally, it seamlessly and optimally selects a predefined number of classifiers from a given set. We evaluate and compare our MIP-based method against six well-established weighting schemes, using representative datasets and suitable metrics, under various ensemble sizes. The experimental results reveal that MIP outperforms all existing approaches, achieving an improvement in balanced accuracy ranging from 0.99% to 7.31%, with an overall average of 4.53% across all datasets and ensemble sizes. Furthermore, it attains an overall average increase of 4.63%, 4.60%, and 4.61% in macro-averaged precision, recall, and F1-score, respectively, while maintaining computational efficiency.

[LG-47] Pattern Matching in AI Compilers and its Formalization (Extended Version)

链接: https://arxiv.org/abs/2412.13398
作者: Joseph W. Cutler,Alex Collins,Bin Fan,Mahesh Ravishankar,Vinod Grover
关键词: Python-based domain specific, machine learning computation, Python-based domain, learning computation graphs, domain specific language
类目: Programming Languages (cs.PL); Machine Learning (cs.LG)
*备注: To appear at CGO’25

点击查看摘要

Abstract:PyPM is a Python-based domain specific language (DSL) for building rewrite-based optimization passes on machine learning computation graphs. Users define individual optimizations by writing (a) patterns that match subgraphs of a computation graph and (b) corresponding rules which replace a matched subgraph with an optimized kernel. PyPM is distinguished from the many other DSLs for defining rewriting passes by its complex and novel pattern language which borrows concepts from logic programming. PyPM patterns can be recursive, nondeterminstic, and can require checking domain-specific constraints such as the shapes of tensors. The PyPM implementation is thus similarly complicated, consisting of thousands of lines of C++ code. In this paper, we present our work on building PyPM, as well as formalizing and distilling and this complexity to an understandable mathematical core. We have developed a formal core calculus expressing the main operations of the PyPM pattern language. We define both a declarative semantics - describing which patterns match which terms - and an algorithmic semantics - an idealized version of the PyPM pattern interpreter - and prove their equivalence. The development is fully mechanized in the Coq proof assistant.

[LG-48] Wind Speed Forecasting Based on Data Decomposition and Deep Learning Models: A Case Study of a Wind Farm in Saudi Arabia

链接: https://arxiv.org/abs/2412.13356
作者: Yasmeen Aldossary,Nabil Hewahi,Abdulla Alasaadi
关键词: energy source, wind speed, hybrid decomposition method, industrial and technological, technological development
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:With industrial and technological development and the increasing demand for electric power, wind energy has gradually become the fastest-growing and most environmentally friendly new energy source. Nevertheless, wind power generation is always accompanied by uncertainty due to the wind speed’s volatility. Wind speed forecasting (WSF) is essential for power grids’ dispatch, stability, and controllability, and its accuracy is crucial to effectively using wind resources. Therefore, this study proposes a novel WSF framework for stationary data based on a hybrid decomposition method and the Bidirectional Long Short-term Memory (BiLSTM) to achieve high forecasting accuracy for the Dumat Al-Jandal wind farm in Al-Jouf, Saudi Arabia. The hybrid decomposition method combines the Wavelet Packet Decomposition (WPD) and the Seasonal Adjustment Method (SAM). The SAM method eliminates the seasonal component of the decomposed subseries generated by WPD to reduce forecasting complexity. The BiLSTM is applied to forecast all the deseasonalized decomposed subseries. Five years of hourly wind speed observations acquired from a location in the Al-Jouf region were used to prove the effectiveness of the proposed model. The comparative experimental results, including 27 other models, demonstrated the proposed model’s superiority in single and multiple WSF with an overall average mean absolute error of 0.176549, root mean square error of 0.247069, and R-squared error of 0.985987.

[LG-49] Concept-ROT: Poisoning Concepts in Large Language Models with Model Editing

链接: https://arxiv.org/abs/2412.13341
作者: Keltin Grimes,Marco Christiani,David Shriver,Marissa Connor
关键词: Large Language Models, Large Language, methods modify specific, modify specific behaviors, Language Models
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注:

点击查看摘要

Abstract:Model editing methods modify specific behaviors of Large Language Models by altering a small, targeted set of network weights and require very little data and compute. These methods can be used for malicious applications such as inserting misinformation or simple trojans that result in adversary-specified behaviors when a trigger word is present. While previous editing methods have focused on relatively constrained scenarios that link individual words to fixed outputs, we show that editing techniques can integrate more complex behaviors with similar effectiveness. We develop Concept-ROT, a model editing-based method that efficiently inserts trojans which not only exhibit complex output behaviors, but also trigger on high-level concepts – presenting an entirely new class of trojan attacks. Specifically, we insert trojans into frontier safety-tuned LLMs which trigger only in the presence of concepts such as ‘computer science’ or ‘ancient civilizations.’ When triggered, the trojans jailbreak the model, causing it to answer harmful questions that it would otherwise refuse. Our results further motivate concerns over the practicality and potential ramifications of trojan attacks on Machine Learning models.

[LG-50] Beyond Accuracy: On the Effects of Fine-tuning Towards Vision-Language Models Prediction Rationality AAAI

链接: https://arxiv.org/abs/2412.13333
作者: Qitong Wang,Tang Li,Kien X. Nguyen,Xi Peng
关键词: Vision-Language Models, widespread applications, Models, CLIP, VLMs
类目: Machine Learning (cs.LG)
*备注: In Proceedings of the Association for the Advancement of Artificial Intelligence (AAAI), 2025

点击查看摘要

Abstract:Vision-Language Models (VLMs), such as CLIP, have already seen widespread applications. Researchers actively engage in further fine-tuning VLMs in safety-critical domains. In these domains, prediction rationality is crucial: the prediction should be correct and based on valid evidence. Yet, for VLMs, the impact of fine-tuning on prediction rationality is seldomly investigated. To study this problem, we proposed two new metrics called Prediction Trustworthiness and Inference Reliability. We conducted extensive experiments on various settings and observed some interesting phenomena. On the one hand, we found that the well-adopted fine-tuning methods led to more correct predictions based on invalid evidence. This potentially undermines the trustworthiness of correct predictions from fine-tuned VLMs. On the other hand, having identified valid evidence of target objects, fine-tuned VLMs were more likely to make correct predictions. Moreover, the findings are also consistent under distributional shifts and across various experimental settings. We hope our research offer fresh insights to VLM fine-tuning.

[LG-51] LossLens: Diagnostics for Machine Learning through Loss Landscape Visual Analytics

链接: https://arxiv.org/abs/2412.13321
作者: Tiankai Xie,Jiaqing Chen,Yaoqing Yang,Caleb Geniesse,Ge Shi,Ajinkya Chaudhari,John Kevin Cava,Michael W. Mahoney,Talita Perciano,Gunther H. Weber,Ross Maciejewski
关键词: Modern machine learning, learn complex features, Modern machine, loss landscape, loss function
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Modern machine learning often relies on optimizing a neural network’s parameters using a loss function to learn complex features. Beyond training, examining the loss function with respect to a network’s parameters (i.e., as a loss landscape) can reveal insights into the architecture and learning process. While the local structure of the loss landscape surrounding an individual solution can be characterized using a variety of approaches, the global structure of a loss landscape, which includes potentially many local minima corresponding to different solutions, remains far more difficult to conceptualize and visualize. To address this difficulty, we introduce LossLens, a visual analytics framework that explores loss landscapes at multiple scales. LossLens integrates metrics from global and local scales into a comprehensive visual representation, enhancing model diagnostics. We demonstrate LossLens through two case studies: visualizing how residual connections influence a ResNet-20, and visualizing how physical parameters influence a physics-informed neural network (PINN) solving a simple convection problem.

[LG-52] Automated Phytosensing: Ozone Exposure Classification Based on Plant Electrical Signals

链接: https://arxiv.org/abs/2412.13312
作者: Till Aust,Eduard Buss,Felix Mohr,Heiko Hamann
关键词: project WatchPlant, environmental state, decentralized network, network of living, air-quality sensors
类目: Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注: Submitted and Accepted at 2025 IEEE Symposia on CI for Energy, Transport and Environmental Sustainability (IEEE CIETES)

点击查看摘要

Abstract:In our project WatchPlant, we propose to use a decentralized network of living plants as air-quality sensors by measuring their electrophysiology to infer the environmental state, also called phytosensing. We conducted in-lab experiments exposing ivy (Hedera helix) plants to ozone, an important pollutant to monitor, and measured their electrophysiological response. However, there is no well established automated way of detecting ozone exposure in plants. We propose a generic automatic toolchain to select a high-performance subset of features and highly accurate models for plant electrophysiology. Our approach derives plant- and stimulus-generic features from the electrophysiological signal using the tsfresh library. Based on these features, we automatically select and optimize machine learning models using AutoML. We use forward feature selection to increase model performance. We show that our approach successfully classifies plant ozone exposure with accuracies of up to 94.6% on unseen data. We also show that our approach can be used for other plant species and stimuli. Our toolchain automates the development of monitoring algorithms for plants as pollutant monitors. Our results help implement significant advancements for phytosensing devices contributing to the development of cost-effective, high-density urban air monitoring systems in the future.

[LG-53] GPgym: A Remote Service Platform with Gaussian Process Regression for Online Learning

链接: https://arxiv.org/abs/2412.13276
作者: Xiaobing Dai,Zewen Yang
关键词: including industry, Machine learning, widely applied, Machine, learning
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Machine learning is now widely applied across various domains, including industry, engineering, and research. While numerous mature machine learning models have been open-sourced on platforms like GitHub, their deployment often requires writing scripts in specific programming languages, such as Python, C++, or MATLAB. This dependency on particular languages creates a barrier for professionals outside the field of machine learning, making it challenging to integrate these algorithms into their workflows. To address this limitation, we propose GPgym, a remote service node based on Gaussian process regression. GPgym enables experts from diverse fields to seamlessly and flexibly incorporate machine learning techniques into their existing specialized software, without needing to write or manage complex script code.

[LG-54] Content-aware Balanced Spectrum Encoding in Masked Modeling for Time Series Classification AAAI25

链接: https://arxiv.org/abs/2412.13232
作者: Yudong Han,Haocong Wang,Yupeng Hu,Yongshun Gong,Xuemeng Song,Weili Guan
关键词: time-series classification task, Masked Time-series Modeling, global dependency, existing transformer-based MTM, transformer-based MTM methods
类目: Machine Learning (cs.LG)
*备注: 13 pages, Accepted by AAAI 25

点击查看摘要

Abstract:Due to the superior ability of global dependency, transformer and its variants have become the primary choice in Masked Time-series Modeling (MTM) towards time-series classification task. In this paper, we experimentally analyze that existing transformer-based MTM methods encounter with two under-explored issues when dealing with time series data: (1) they encode features by performing long-dependency ensemble averaging, which easily results in rank collapse and feature homogenization as the layer goes deeper; (2) they exhibit distinct priorities in fitting different frequency components contained in the time-series, inevitably leading to spectrum energy imbalance of encoded feature. To tackle these issues, we propose an auxiliary content-aware balanced decoder (CBD) to optimize the encoding quality in the spectrum space within masked modeling scheme. Specifically, the CBD iterates on a series of fundamental blocks, and thanks to two tailored units, each block could progressively refine the masked representation via adjusting the interaction pattern based on local content variations of time-series and learning to recalibrate the energy distribution across different frequency components. Moreover, a dual-constraint loss is devised to enhance the mutual optimization of vanilla decoder and our CBD. Extensive experimental results on ten time-series classification datasets show that our method nearly surpasses a bunch of baselines. Meanwhile, a series of explanatory results are showcased to sufficiently demystify the behaviors of our method.

[LG-55] Cross-table Synthetic Tabular Data Detection

链接: https://arxiv.org/abs/2412.13227
作者: G. Charbel N. Kindji(LACODAM),Lina Maria Rojas-Barahona,Elisa Fromont(LACODAM),Tanguy Urvoy
关键词: Detecting synthetic tabular, compromise data-driven decision-making, Detecting synthetic, synthetic tabular data, data-driven decision-making
类目: Machine Learning (cs.LG); Databases (cs.DB); Neural and Evolutionary Computing (cs.NE)
*备注:

点击查看摘要

Abstract:Detecting synthetic tabular data is essential to prevent the distribution of false or manipulated datasets that could compromise data-driven decision-making. This study explores whether synthetic tabular data can be reliably identified ‘‘in the wild’’-meaning across different generators, domains, and table formats. This challenge is unique to tabular data, where structures (such as number of columns, data types, and formats) can vary widely from one table to another. We propose three cross-table baseline detectors and four distinct evaluation protocols, each corresponding to a different level of ‘‘wildness’’. Our very preliminary results confirm that cross-table adaptation is a challenging task.

[LG-56] jinns: a JAX Library for Physics-Informed Neural Networks

链接: https://arxiv.org/abs/2412.14132
作者: Hugo Gangloff,Nicolas Jouvin
关键词: open-source Python library, physics-informed neural networks, open-source Python, Python library, neural networks
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 12 pages, 1 figure

点击查看摘要

Abstract:jinns is an open-source Python library for physics-informed neural networks, built to tackle both forward and inverse problems, as well as meta-model learning. Rooted in the JAX ecosystem, it provides a versatile framework for efficiently prototyping real-problems, while easily allowing extensions to specific needs. Furthermore, the implementation leverages existing popular JAX libraries such as equinox and optax for model definition and optimisation, bringing a sense of familiarity to the user. Many models are available as baselines, and the documentation provides reference implementations of different use-cases along with step-by-step tutorials for extensions to specific needs. The code is available on Gitlab this https URL.

[LG-57] Spatio-Temporal SIR Model of Pandemic Spread During Warfare with Optimal Dual-use Healthcare System Administration using Deep Reinforcement Learning

链接: https://arxiv.org/abs/2412.14039
作者: Adi Shuchami,Teddy Lazebnik
关键词: shaped human history, repeatedly shaped human, simultaneous occurrence presents, occurrence presents profound, presents profound challenges
类目: Quantitative Methods (q-bio.QM); Machine Learning (cs.LG); Multiagent Systems (cs.MA); Physics and Society (physics.soc-ph)
*备注:

点击查看摘要

Abstract:Large-scale crises, including wars and pandemics, have repeatedly shaped human history, and their simultaneous occurrence presents profound challenges to societies. Understanding the dynamics of epidemic spread during warfare is essential for developing effective containment strategies in complex conflict zones. While research has explored epidemic models in various settings, the impact of warfare on epidemic dynamics remains underexplored. In this study, we proposed a novel mathematical model that integrates the epidemiological SIR (susceptible-infected-recovered) model with the war dynamics Lanchester model to explore the dual influence of war and pandemic on a population’s mortality. Moreover, we consider a dual-use military and civil healthcare system that aims to reduce the overall mortality rate which can use different administration policies. Using an agent-based simulation to generate in silico data, we trained a deep reinforcement learning model for healthcare administration policy and conducted an intensive investigation on its performance. Our results show that a pandemic during war conduces chaotic dynamics where the healthcare system should either prioritize war-injured soldiers or pandemic-infected civilians based on the immediate amount of mortality from each option, ignoring long-term objectives. Our findings highlight the importance of integrating conflict-related factors into epidemic modeling to enhance preparedness and response strategies in conflict-affected areas.

[LG-58] Variance-based loss function for improved regularization

链接: https://arxiv.org/abs/2412.13993
作者: John M. Hanna,Irene E. Vignon-Clemental
关键词: chosen error metric, deep learning, squared or absolute, loss function, error metric
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:In deep learning, the mean of a chosen error metric, such as squared or absolute error, is commonly used as a loss function. While effective in reducing the average error, this approach often fails to address localized outliers, leading to significant inaccuracies in regions with sharp gradients or discontinuities. This issue is particularly evident in physics-informed neural networks (PINNs), where such localized errors are expected and affect the overall solution. To overcome this limitation, we propose a novel loss function that combines the mean and the standard deviation of the chosen error metric. By minimizing this combined loss function, the method ensures a more uniform error distribution and reduces the impact of localized high-error regions. The proposed loss function was tested on three problems: Burger’s equation, 2D linear elastic solid mechanics, and 2D steady Navier-Stokes, demonstrating improved solution quality and lower maximum errors compared to the standard mean-based loss, using the same number of iterations and weight initialization.

[LG-59] LeStrat-Net: Lebesgue style stratification for Monte Carlo simulations powered by machine learning

链接: https://arxiv.org/abs/2412.13982
作者: Kayoung Ban,Myeonghun Park,Raymundo Ramos
关键词: Monte Carlo sampling, machine learning algorithm, Monte Carlo, Carlo sampling, stratification in Monte
类目: High Energy Physics - Phenomenology (hep-ph); Machine Learning (cs.LG)
*备注: 44 pages, 17 figures

点击查看摘要

Abstract:We develop a machine learning algorithm to turn around stratification in Monte Carlo sampling. We use a different way to divide the domain space of the integrand, based on the height of the function being sampled, similar to what is done in Lebesgue integration. This means that isocontours of the function define regions that can have any shape depending on the behavior of the function. We take advantage of the capacity of neural networks to learn complicated functions in order to predict these complicated divisions and preclassify large samples of the domain space. From this preclassification we can select the required number of points to perform a number of tasks such as variance reduction, integration and even event selection. The network ultimately defines the regions with what it learned and is also used to calculate the multi-dimensional volume of each region.

[LG-60] Model-Agnostic Cosmological Inference with SDSS-IV eBOSS: Simultaneous Probing for Background and Perturbed Universe

链接: https://arxiv.org/abs/2412.13973
作者: Purba Mukherjee,Anjan A. Sen
关键词: Sloan Digital Sky, completed Sloan Digital, Digital Sky Survey, Baryon Oscillation Spectroscopic, Oscillation Spectroscopic Survey
类目: Cosmology and Nongalactic Astrophysics (astro-ph.CO); Machine Learning (cs.LG); General Relativity and Quantum Cosmology (gr-qc)
*备注: 13 pages, 7 sets of figures, 3 tables. Comments are welcome

点击查看摘要

Abstract:Here we explore certain subtle features imprinted in data from the completed Sloan Digital Sky Survey IV (SDSS-IV) extended Baryon Oscillation Spectroscopic Survey (eBOSS) as a combined probe for the background and perturbed Universe. We reconstruct the baryon Acoustic Oscillation (BAO) and Redshift Space Distortion (RSD) observables as functions of redshift, using measurements from SDSS alone. We apply the Multi-Task Gaussian Process (MTGP) framework to model the interdependencies of cosmological observables D_M(z)/r_d , D_H(z)/r_d , and f\sigma_8(z) , and track their evolution across different redshifts. Subsequently, we obtain constrained three-dimensional phase space containing D_M(z)/r_d , D_H(z)/r_d , and f\sigma_8(z) at different redshifts probed by the SDSS-IV eBOSS survey. Furthermore, assuming the \Lambda CDM model, we obtain constraints on model parameters \Omega_m , H_0r_d , \sigma_8 and S_8 at each redshift probed by SDSS-IV eBOSS. This indicates redshift-dependent trends in H_0 , \Omega_m , \sigma_8 and S_8 in the \Lambda CDM model, suggesting a possible inconsistency in the \Lambda CDM model. Ours is a template for model-independent extraction of information for both background and perturbed Universe using a single galaxy survey taking into account all the existing correlations between background and perturbed observables and this can be easily extended to future DESI-3YR as well as Euclid results.

[LG-61] Investigating the Effects of Diffusion-based Conditional Generative Speech Models Used for Speech Enhancement on Dysarthric Speech ICASSP2025

链接: https://arxiv.org/abs/2412.13933
作者: Joanna Reszka,Parvaneh Janbakhshi,Tilak Purohit,Sadegh Mohammadi
关键词: Parkinson disease recorded, due to Parkinson, Parkinson disease, dysarthric speech, speech
类目: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Sound (cs.SD)
*备注: Accepted at ICASSP 2025 Satellite Workshop: Workshop on Speech Pathology Analysis and DEtection (SPADE)

点击查看摘要

Abstract:In this study, we aim to explore the effect of pre-trained conditional generative speech models for the first time on dysarthric speech due to Parkinson’s disease recorded in an ideal/non-noisy condition. Considering one category of generative models, i.e., diffusion-based speech enhancement, these models are previously trained to learn the distribution of clean (i.e, recorded in a noise-free environment) typical speech signals. Therefore, we hypothesized that when being exposed to dysarthric speech they might remove the unseen atypical paralinguistic cues during the enhancement process. By considering the automatic dysarthric speech detection task, in this study, we experimentally show that during the enhancement process of dysarthric speech data recorded in an ideal non-noisy environment, some of the acoustic dysarthric speech cues are lost. Therefore such pre-trained models are not yet suitable in the context of dysarthric speech enhancement since they manipulate the pathological speech cues when they process clean dysarthric speech. Furthermore, we show that the removed acoustics cues by the enhancement models in the form of residue speech signal can provide complementary dysarthric cues when fused with the original input speech signal in the feature space.

[LG-62] Preconditioned Subspace Langevin Monte Carlo

链接: https://arxiv.org/abs/2412.13928
作者: Tyler Maunu,Jiayi Yao
关键词: Langevin Monte Carlo, Preconditioned Langevin Monte, Subspace Langevin Monte, Monte Carlo, Langevin Monte
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 19 pages, 2 figures, 1 table

点击查看摘要

Abstract:We develop a new efficient method for high-dimensional sampling called Subspace Langevin Monte Carlo. The primary application of these methods is to efficiently implement Preconditioned Langevin Monte Carlo. To demonstrate the usefulness of this new method, we extend ideas from subspace descent methods in Euclidean space to solving a specific optimization problem over Wasserstein space. Our theoretical analysis demonstrates the advantageous convergence regimes of the proposed method, which depend on relative conditioning assumptions common to mirror descent methods. We back up our theory with experimental evidence on sampling from an ill-conditioned Gaussian distribution.

[LG-63] Speech Watermarking with Discrete Intermediate Representations AAAI2025

链接: https://arxiv.org/abs/2412.13917
作者: Shengpeng Ji,Ziyue Jiang,Jialong Zuo,Minghui Fang,Yifu Chen,Tao Jin,Zhou Zhao
关键词: potential harmful consequences, proactively mitigate, mitigate the potential, potential harmful, harmful consequences
类目: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Sound (cs.SD); Signal Processing (eess.SP)
*备注: Accepted by AAAI 2025

点击查看摘要

Abstract:Speech watermarking techniques can proactively mitigate the potential harmful consequences of instant voice cloning techniques. These techniques involve the insertion of signals into speech that are imperceptible to humans but can be detected by algorithms. Previous approaches typically embed watermark messages into continuous space. However, intuitively, embedding watermark information into robust discrete latent space can significantly improve the robustness of watermarking systems. In this paper, we propose DiscreteWM, a novel speech watermarking framework that injects watermarks into the discrete intermediate representations of speech. Specifically, we map speech into discrete latent space with a vector-quantized autoencoder and inject watermarks by changing the modular arithmetic relation of discrete IDs. To ensure the imperceptibility of watermarks, we also propose a manipulator model to select the candidate tokens for watermark embedding. Experimental results demonstrate that our framework achieves state-of-the-art performance in robustness and imperceptibility, simultaneously. Moreover, our flexible frame-wise approach can serve as an efficient solution for both voice cloning detection and information hiding. Additionally, DiscreteWM can encode 1 to 150 bits of watermark information within a 1-second speech clip, indicating its encoding capacity. Audio samples are available at this https URL.

[LG-64] Data-driven Discovery of Biophysical T Cell Receptor Co-specificity Rules

链接: https://arxiv.org/abs/2412.13722
作者: Andrew G.T. Pyo,Yuta Nagano,Martina Milighetti,James Henderson,Curtis G. Callan Jr.,Benny Chain,Ned S. Wingreen,Andreas Tiffeau-Mayer
关键词: cellular immune response, biophysical interactions, cell receptor, ligands, TCRs share specificity
类目: Biomolecules (q-bio.BM); Machine Learning (cs.LG); Biological Physics (physics.bio-ph)
*备注: 15 pages, 10 figures

点击查看摘要

Abstract:The biophysical interactions between the T cell receptor (TCR) and its ligands determine the specificity of the cellular immune response. However, the immense diversity of receptors and ligands has made it challenging to discover generalizable rules across the distinct binding affinity landscapes created by different ligands. Here, we present an optimization framework for discovering biophysical rules that predict whether TCRs share specificity to a ligand. Applying this framework to TCRs associated with a collection of SARS-CoV-2 peptides we establish how co-specificity depends on the type and position of amino-acid differences between receptors. We also demonstrate that the inferred rules generalize to ligands not seen during training. Our analysis reveals that matching of steric properties between substituted amino acids is important for receptor co-specificity, in contrast with the hydrophobic properties that more prominently determine evolutionary substitutability. We furthermore find that positions not in direct contact with the peptide still significantly impact specificity. These findings highlight the potential for data-driven approaches to uncover the molecular mechanisms underpinning the specificity of adaptive immune responses.

[LG-65] Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA NEURIPS2024

链接: https://arxiv.org/abs/2412.13716
作者: Lifeng Qiao,Peng Ye,Yuchen Ren,Weiqiang Bai,Chaoqi Liang,Xinzhu Ma,Nanqing Dong,Wanli Ouyang
关键词: made significant strides, Foundation models, DNA sequences, DNA sequences due, made significant
类目: Genomics (q-bio.GN); Machine Learning (cs.LG)
*备注: Accepted by NeurIPS 2024

点击查看摘要

Abstract:Foundation models have made significant strides in understanding the genomic language of DNA sequences. However, previous models typically adopt the tokenization methods designed for natural language, which are unsuitable for DNA sequences due to their unique characteristics. In addition, the optimal approach to tokenize DNA remains largely under-explored, and may not be intuitively understood by humans even if discovered. To address these challenges, we introduce MxDNA, a novel framework where the model autonomously learns an effective DNA tokenization strategy through gradient decent. MxDNA employs a sparse Mixture of Convolution Experts coupled with a deformable convolution to model the tokenization process, with the discontinuous, overlapping, and ambiguous nature of meaningful genomic segments explicitly considered. On Nucleotide Transformer Benchmarks and Genomic Benchmarks, MxDNA demonstrates superior performance to existing methods with less pretraining data and time, highlighting its effectiveness. Finally, we show that MxDNA learns unique tokenization strategy distinct to those of previous methods and captures genomic functionalities at a token level during self-supervised pretraining. Our MxDNA aims to provide a new perspective on DNA tokenization, potentially offering broad applications in various domains and yielding profound insights.

[LG-66] me-Reversible Bridges of Data with Machine Learning

链接: https://arxiv.org/abs/2412.13665
作者: Ludwig Winkler
关键词: sciences and engineering, dynamics, analysis of dynamical, fundamental tool, natural sciences
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:The analysis of dynamical systems is a fundamental tool in the natural sciences and engineering. It is used to understand the evolution of systems as large as entire galaxies and as small as individual molecules. With predefined conditions on the evolution of dy-namical systems, the underlying differential equations have to fulfill specific constraints in time and space. This class of problems is known as boundary value problems. This thesis presents novel approaches to learn time-reversible deterministic and stochastic dynamics constrained by initial and final conditions. The dynamics are inferred by machine learning algorithms from observed data, which is in contrast to the traditional approach of solving differential equations by numerical integration. The work in this thesis examines a set of problems of increasing difficulty each of which is concerned with learning a different aspect of the dynamics. Initially, we consider learning deterministic dynamics from ground truth solutions which are constrained by deterministic boundary conditions. Secondly, we study a boundary value problem in discrete state spaces, where the forward dynamics follow a stochastic jump process and the boundary conditions are discrete probability distributions. In particular, the stochastic dynamics of a specific jump process, the Ehrenfest process, is considered and the reverse time dynamics are inferred with machine learning. Finally, we investigate the problem of inferring the dynamics of a continuous-time stochastic process between two probability distributions without any reference information. Here, we propose a novel criterion to learn time-reversible dynamics of two stochastic processes to solve the Schrödinger Bridge Problem.

[LG-67] Forward and Inverse Simulation of Pseudo-Two-Dimensional Model of Lithium-Ion Batteries Using Neural Networks

链接: https://arxiv.org/abs/2412.13200
作者: Myeong-Su Lee,Jaemin Oh,Dong-Chan Lee,KangWook Lee,Sooncheol Park,Youngjoon Hong
关键词: physics-informed neural network, neural network, high nonlinearity, physics-informed neural, PINN loss function
类目: Computational Physics (physics.comp-ph); Machine Learning (cs.LG)
*备注: 26 pages, 10 figures, 3 tables

点击查看摘要

Abstract:In this work, we address the challenges posed by the high nonlinearity of the Butler-Volmer (BV) equation in forward and inverse simulations of the pseudo-two-dimensional (P2D) model using the physics-informed neural network (PINN) framework. The BV equation presents significant challenges for PINNs, primarily due to the hyperbolic sine term, which renders the Hessian of the PINN loss function highly ill-conditioned. To address this issue, we introduce a bypassing term that improves numerical stability by substantially reducing the condition number of the Hessian matrix. Furthermore, the small magnitude of the ionic flux ( j ) often leads to a common failure mode where PINNs converge to incorrect solutions. We demonstrate that incorporating a secondary conservation law for the solid-phase potential ( \psi ) effectively prevents such convergence issues and ensures solution accuracy. The proposed methods prove effective for solving both forward and inverse problems involving the BV equation. Specifically, we achieve precise parameter estimation in inverse scenarios and reliable solution predictions for forward simulations.

信息检索

[IR-0] Adversarial Hubness in Multi-Modal Retrieval

链接: https://arxiv.org/abs/2412.14113
作者: Tingwei Zhang,Fnu Suya,Rishi Jha,Collin Zhang,Vitaly Shmatikov
关键词: high-dimensional vector spaces, phenomenon in high-dimensional, distribution is unusually, unusually close, adversarial
类目: Cryptography and Security (cs.CR); Information Retrieval (cs.IR)
*备注:

点击查看摘要

Abstract:Hubness is a phenomenon in high-dimensional vector spaces where a single point from the natural distribution is unusually close to many other points. This is a well-known problem in information retrieval that causes some items to accidentally (and incorrectly) appear relevant to many queries. In this paper, we investigate how attackers can exploit hubness to turn any image or audio input in a multi-modal retrieval system into an adversarial hub. Adversarial hubs can be used to inject universal adversarial content (e.g., spam) that will be retrieved in response to thousands of different queries, as well as for targeted attacks on queries related to specific, attacker-chosen concepts. We present a method for creating adversarial hubs and evaluate the resulting hubs on benchmark multi-modal retrieval datasets and an image-to-image retrieval system based on a tutorial from Pinecone, a popular vector database. For example, in text-caption-to-image retrieval, a single adversarial hub is retrieved as the top-1 most relevant image for more than 21,000 out of 25,000 test queries (by contrast, the most common natural hub is the top-1 response to only 102 queries). We also investigate whether techniques for mitigating natural hubness are an effective defense against adversarial hubs, and show that they are not effective against hubs that target queries related to specific concepts.

[IR-1] A Cognitive Ideation Support Framework using IBM Watson Services

链接: https://arxiv.org/abs/2412.14025
作者: Samaa Elnagar,Kweku-Muata Osei-Bryson
关键词: core activity, activity for innovation, IBM Watson, knowledge bases, organizations’ knowledge bases
类目: Information Retrieval (cs.IR)
*备注: Twenty-fifth Americas Conference on Information Systems (AMCIS 2019), Cancun, 2019

点击查看摘要

Abstract:Ideas generation is a core activity for innovation in organizations. The creativity of the generated ideas depends not only on the knowledge retrieved from the organizations’ knowledge bases, but also on the external knowledge retrieved from other resources. Unfortunately, organizations often cannot efficiently utilize the knowledge in the knowledge bases due to the limited abilities of the search and retrieval mechanisms especially when dealing with unstructured data. In this paper, we present a new cognitive support framework for ideation that uses the IBM Watson DeepQA services. IBM Watson is a Question Answering system which mimics human cognitive abilities to retrieve and rank information. The proposed framework is based on the Search for Ideas in the Associative Memory (SIAM) model to help organizations develop creative ideas through discovering new relationships between retrieved data. To evaluate the effectiveness of the proposed system, the generated ideas generated are selected and assessed using a set of established creativity criteria.

[IR-2] JudgeBlender: Ensembling Judgments for Automatic Relevance Assessment

链接: https://arxiv.org/abs/2412.13268
作者: Hossein A. Rahmani,Emine Yilmaz,Nick Craswell,Bhaskar Mitra
关键词: retrieval systems require, human assessors, costly and time-consuming, require a substantial, substantial amount
类目: Information Retrieval (cs.IR)
*备注: 14 pages

点击查看摘要

Abstract:The effective training and evaluation of retrieval systems require a substantial amount of relevance judgments, which are traditionally collected from human assessors – a process that is both costly and time-consuming. Large Language Models (LLMs) have shown promise in generating relevance labels for search tasks, offering a potential alternative to manual assessments. Current approaches often rely on a single LLM, such as GPT-4, which, despite being effective, are expensive and prone to intra-model biases that can favour systems leveraging similar models. In this work, we introduce JudgeBlender, a framework that employs smaller, open-source models to provide relevance judgments by combining evaluations across multiple LLMs (LLMBlender) or multiple prompts (PromptBlender). By leveraging the LLMJudge benchmark [18], we compare JudgeBlender with state-of-the-art methods and the top performers in the LLMJudge challenge. Our results show that JudgeBlender achieves competitive performance, demonstrating that very large models are often unnecessary for reliable relevance assessments.

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自然语言处理

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