Relation-R1: 逐步认知链式思维引导的强化学习用于统一关系理解
Relation-R1: Progressively Cognitive Chain-of-Thought Guided Reinforcement Learning for Unified Relation Comprehension
AI总结：
Relation-R1通过整合认知链式思维引导的强化学习，实现了对二元和N元关系的统一理解，提升了视觉-语义定位能力。
摘要：
近期多模态大语言模型（MLLMs）的进展显著提升了物体层面的定位和区域描述。然而，它们在视觉关系理解方面仍然受限，甚至在二元关系检测上也存在困难，更不用说涉及多个语义角色的N元关系了。核心原因是缺乏对多实体之间结构语义依赖关系的建模，导致输出不可靠、幻觉和过度依赖语言先验（例如，默认选择
英文摘要：
Recent advances in multi-modal large language models (MLLMs) have significantly improved object-level grounding and region captioning. However, they remain limited in visual relation understanding, struggling even with binary relation detection, let alone \textit{N}-ary relations involving multiple semantic roles. The core reason is the lack of modeling for \textit{structural semantic dependencies} among multi-entities, leading to unreliable outputs, hallucinations, and over-reliance on language priors (\eg, defaulting to ``person drinks a milk'' if a person is merely holding it). To this end, we propose Relation-R1, the \textit{first unified} relation comprehension framework that explicitly integrates cognitive chain-of-thought (CoT)-guided supervised fine-tuning (SFT) and group relative policy optimization (GRPO) within a reinforcement learning (RL) paradigm. Specifically, we first establish foundational reasoning capabilities via SFT, enforcing structured outputs with thinking processes. Then, GRPO is utilized to refine these outputs via multi-rewards optimization, prioritizing visual-semantic grounding over language-induced biases, thereby improving generalization capability. Furthermore, we investigate the impact of various CoT strategies within this framework, demonstrating that a specific-to-general progressive approach in CoT guidance further improves generalization, especially in capturing synonymous \textit{N}-ary relations. Extensive experiments on widely-used PSG and SWiG datasets demonstrate that Relation-R1 achieves state-of-the-art performance in both binary and \textit{N}-ary relation understanding.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
