Region-R1: Reinforcing Query-Side Region Cropping for Multi-Modal Re-Ranking
Multi-modal retrieval-augmented generation (MM-RAG) relies heavily on re-rankers to surface the most relevant evidence for image-question queries. However, standard re-rankers typically process the full query image as a global embedding, making them susceptible to visual distractors (e.g., background clutter) that skew similarity scores. We propose Region-R1, a query-side region cropping framework that formulates region selection as a decision-making problem during re-ranking, allowing the system to learn to retain the full image or focus only on a question-relevant region before scoring the retrieved candidates. Region-R1 learns a policy with a novel region-aware group relative policy op...
| Authors | Chan-Wei Hu, Zhengzhong Tu |
|---|---|
| Primary category | cs.CV |
| Published | 7 April 2026 |
| Last updated at source | 8 April 2026 |
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