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arXiv 2603.11631cs.AIcs.CV
VisDoT : 通过类人解释 grounding 和思维分解增强视觉推理
VisDoT : Enhancing Visual Reasoning through Human-Like Interpretation Grounding and Decomposition of Thought
Department of Computer Science and Artificial Intelligence, Dongguk University（东国大学计算机科学与人工智能系）；Department of Electronics and Electrical Engineering, Dongguk University（东国大学电子与电气工程系）
Eunsoo Lee, Jeongwoo Lee, Minki Hong, Jangho Choi, Jihie Kim
Large vision-language models (LVLMs) struggle to reliably detect visual primitives in charts and align them with semantic representations, which severely limits their performance on complex visual reasoning. This lack of perceptual grounding constitutes a major bottleneck for chart-based reasoning. We propose VisDoT, a framework that enhances visual reasoning through human-like interpretation grounding. We formalize four perceptual tasks based on the theory of graphical perception, including position and length. Building on this foundation, we introduce Decomposition-of-Thought (DoT) prompting, which sequentially separates questions into visual perception sub-questions and logic sub-questions. Fine-tuning InternVL with VisDoT achieves a +11.2% improvement on ChartQA and surpasses GPT-4o on the more challenging ChartQAPro benchmark. On the newly introduced VisDoTQA benchmark, the model improves by +33.2%. Furthermore, consistent zero-shot gains on diverse open-domain VQA benchmarks confirm the generalizability of the perception-logic separation strategy for visual question answering. VisDoT leverages human-like perception to enhance visual grounding, achieving state-of-the-art chart understanding and interpretable visual reasoning.
Better Eyes, Better Thoughts: Why Vision Chain-of-Thought Fails in Medicine
Department of Computer Science, City University of Hong Kong (Dongguan)（香港城市大学（东莞）计算机科学系）；College of Computing and Data Science (CCDS), Nanyang Technological University（南洋理工大学计算与数据科学学院）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机科学学院）
Mixture-of-Thought-Tokens: Unifying Perception and Reasoning for Free-form Multimodal Grounding
Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（西安交通大学人工智能与机器人研究所）；Xingchen AGI Lab, China Telecom Artificial Intelligence Technology (Beijing) Co., Ltd（星辰通用人工智能实验室，中国电信人工智能技术（北京）有限公司）；University of Science and Technology Beijing（北京科技大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Shanghai Jiao Tong University（上海交通大学）
RGBX-R1: Visual Modality Chain-of-Thought Guided Reinforcement Learning for Multimodal Grounding
School of Artificial Intelligence, Tianjin University（天津大学人工智能学院）；Low-Altitude Intelligence Lab, Xiong’an National Innovation Center（雄安国家创新中心低空智能实验室）；Xiong’an Guochuang Lantian Technology Co., Ltd.（雄安国创莲田科技有限公司）；College of Electronic Science and Technology, National University of Defense Technology（国防科技大学电子科学学院）
CoTBox-TTT: Grounding Medical VQA with Visual Chain-of-Thought Boxes During Test-time Training
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港大学深圳研究院数据科学学院）
DocVAL: Validated Chain-of-Thought Distillation for Grounded Document VQA
Department of Computer Science（计算机科学系）；Engineering, Ohio State University, Ohio, US（工程系，俄亥俄州立大学，俄亥俄，美国）；Department of Computer Science, University of Central Florida, Florida, US（计算机科学系，中央佛罗里达大学，佛罗里达，美国）
TAR: Temporal Anchor-Constrained Reasoning for Video Temporal Grounding
South China University of Technology（华南理工大学）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东人工智能与数字经济实验室（深圳））；Bytedance Inc.（字节跳动有限公司）
From Concept Alignment to Causal Grounding: An Intervention Test of Chain-of-Thought Faithfulness
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；Saarland Informatics Campus（萨尔兰信息学园区）；Centre for European Research in Trusted AI (CERTAIN)（欧洲可信人工智能研究中心（CERTAIN））；Technische Universität Berlin（柏林工业大学）；IBM Research（IBM研究院）；University of Copenhagen（哥本哈根大学）；University of Groningen（格罗宁根大学）
