# Region-R1: Reinforcing Query-Side Region Cropping for Multi-Modal Re-Ranking

arXiv: https://arxiv.org/abs/2604.05268 · HTML: https://mlsanity.com/paper/2604.05268/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 29 of its 44 references that are in the map.
- First posted on arXiv 7 Apr 2026.

## Placed in

- Retrieval-augmented generation / [Multimodal RAG](https://mlsanity.com/rag/multimodal-rag/)

## Builds on (most cited first, 29 of 29)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge](https://mlsanity.com/paper/1906.00067/) (2019)
- [EVA-CLIP: Improved Training Techniques for CLIP at Scale](https://mlsanity.com/paper/2303.15389/) (2023)
- [A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge](https://mlsanity.com/paper/2206.01718/) (2022)
- [Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval](https://mlsanity.com/paper/2004.01804/) (2020)
- [MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text](https://mlsanity.com/paper/2210.02928/) (2022)
- [Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?](https://mlsanity.com/paper/2302.11713/) (2023)
- [RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs](https://mlsanity.com/paper/2407.02485/) (2024)
- [Benchmarking Representation Learning for Natural World Image Collections](https://mlsanity.com/paper/2103.16483/) (2021)
- [MM-Embed: Universal Multimodal Retrieval with Multimodal LLMs](https://mlsanity.com/paper/2411.02571/) (2024)
- [REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory](https://mlsanity.com/paper/2212.05221/) (2022)
- [A Theoretical Analysis of NDCG Type Ranking Measures](https://mlsanity.com/paper/1304.6480/) (2013)
- [Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entities](https://mlsanity.com/paper/2302.11154/) (2023)
- [Encyclopedic VQA: Visual questions about detailed properties of fine-grained categories](https://mlsanity.com/paper/2306.09224/) (2023)
- [Retrieval Augmented Visual Question Answering with Outside Knowledge](https://mlsanity.com/paper/2210.03809/) (2022)
- [Wiki-LLaVA: Hierarchical Retrieval-Augmented Generation for Multimodal LLMs](https://mlsanity.com/paper/2404.15406/) (2024)
- [Multi-Modal Answer Validation for Knowledge-Based VQA](https://mlsanity.com/paper/2103.12248/) (2021)
- [EchoSight: Advancing Visual-Language Models with Wiki Knowledge](https://mlsanity.com/paper/2407.12735/) (2024)
- [ConvGQR: Generative Query Reformulation for Conversational Search](https://mlsanity.com/paper/2305.15645/) (2023)
- [MARVEL: Unlocking the Multi-Modal Capability of Dense Retrieval via Visual Module Plugin](https://mlsanity.com/paper/2310.14037/) (2023)
- [Enhancing Interactive Image Retrieval With Query Rewriting Using Large Language Models and Vision Language Models](https://mlsanity.com/paper/2404.18746/) (2024)
- [MMKB-RAG: A Multi-Modal Knowledge-Based Retrieval-Augmented Generation Framework](https://mlsanity.com/paper/2504.10074/) (2025)
- [OMGM: Orchestrate Multiple Granularities and Modalities for Efficient Multimodal Retrieval](https://mlsanity.com/paper/2505.07879/) (2025)
- [DocReRank: Single-Page Hard Negative Query Generation for Training Multi-Modal RAG Rerankers](https://mlsanity.com/paper/2505.22584/) (2025)
- [WikiWeb2M: A Page-Level Multimodal Wikipedia Dataset](https://mlsanity.com/paper/2305.05432/) (2023)
- [RAMQA: A Unified Framework for Retrieval-Augmented Multi-Modal Question Answering](https://mlsanity.com/paper/2501.13297/) (2025)
- [Multi-Turn Multi-Modal Question Clarification for Enhanced Conversational Understanding](https://mlsanity.com/paper/2502.11442/) (2025)

## Abstract (arXiv)

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 optimization (r-GRPO) to dynamically crop a discriminative region. Across two challenging benchmarks, E-VQA and InfoSeek, Region-R1 delivers consistent gains, achieving state-of-the-art performances by increasing conditional Recall@1 by up to 20%. These results show the great promise of query-side adaptation as a simple but effective way to strengthen MM-RAG re-ranking.