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About this paper
Wenfang Sun, Hao Chen, Yingjun Du, Yefeng Zheng, Cees G. M. Snoek
RegionReasoner, a reinforcement learning framework, enables multi-round visual reasoning in large vision-language models by enforcing explicit spatial grounding and global-local semantic consistency in structured outputs. The system achieved top performance on a new multi-turn benchmark for referring detection and segmentation, showing improved robustness against error accumulation at deeper conversational turns.
Key insights
- Explicitly grounding reasoning steps to visual regions with verifiable coordinate citations significantly stabilizes reference propagation and reduces hallucination across multiple conversational turns.
- Integrating global and local semantic consistency as a reward signal effectively prevents divergence between overall scene understanding and specific region-focused reasoning, leading to more coherent dialogue traces.
- Reinforcement learning, particularly with structured rewards for intermediate reasoning steps (like reference citation and semantic consistency), is effective for shaping fine-grained behaviors in VLMs for complex multi-round tasks.
Summary by alphaXiv.
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