mlsanityData to 13 Oct 2026

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

arXiv 2604.05268 ↗First posted 7 Apr 2026cs.CV cs.AI cs.CLcited by 0

On the map: Retrieval-augmented generation → Multimodal RAG

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.

Key facts. No citing papers in the map yet (data through 13 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.

Builds on

29 of 44 references.

Closest ancestors · 8

Same direction, newest first

  1. 2025OMGM: Orchestrate Multiple Granularities and Modalities for Efficient Multimodal Retrieval10 May
  2. 2025MMKB-RAG: A Multi-Modal Knowledge-Based Retrieval-Augmented Generation Framework14 Apr
  3. 2025RAMQA: A Unified Framework for Retrieval-Augmented Multi-Modal Question Answering23 Jan
  4. 2024Wiki-LLaVA: Hierarchical Retrieval-Augmented Generation for Multimodal LLMs23 Apr
  5. 2023Encyclopedic VQA: Visual questions about detailed properties of fine-grained categories15 Jun
  6. 2022REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory10 Dec
  7. 2022Retrieval Augmented Visual Question Answering with Outside Knowledge7 Oct
  8. 2022MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text6 Oct

Other references · 21

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,588
  2. 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,299
  3. 2023Sigmoid Loss for Language Image Pre-Trainingcited by 3,068
  4. 2019OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledgecited by 1,116
  5. 2023EVA-CLIP: Improved Training Techniques for CLIP at Scalecited by 792
  6. 2022A-OKVQA: A Benchmark for Visual Question Answering using World Knowledgecited by 706
  7. 2020Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrievalcited by 308
  8. 2023Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?cited by 205
  9. 2024RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMscited by 201
  10. 2021Benchmarking Representation Learning for Natural World Image Collectionscited by 167
  11. 2024MM-Embed: Universal Multimodal Retrieval with Multimodal LLMscited by 160
  12. 2013A Theoretical Analysis of NDCG Type Ranking Measurescited by 119
9 more
  1. 2023Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entitiescited by 112
  2. 2021Multi-Modal Answer Validation for Knowledge-Based VQAcited by 78
  3. 2024EchoSight: Advancing Visual-Language Models with Wiki Knowledgecited by 62
  4. 2023ConvGQR: Generative Query Reformulation for Conversational Searchcited by 59
  5. 2023MARVEL: Unlocking the Multi-Modal Capability of Dense Retrieval via Visual Module Plugincited by 22
  6. 2024Enhancing Interactive Image Retrieval With Query Rewriting Using Large Language Models and Vision Language Modelscited by 19
  7. 2025DocReRank: Single-Page Hard Negative Query Generation for Training Multi-Modal RAG Rerankerscited by 9
  8. 2023WikiWeb2M: A Page-Level Multimodal Wikipedia Datasetcited by 8
  9. 2025Multi-Turn Multi-Modal Question Clarification for Enhanced Conversational Understandingcited by 3

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Not cited yet.

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Close in wording, no citation either way

  1. 2025RegionRAG: Region-level Retrieval-Augmented Generation for Visual Document Understanding
  2. 2026RegRet: Enhancing Region-Level Retrieval in Large Multimodal Models
  3. 2025GRAPE: Let GRPO Supervise Query Rewriting by Ranking for Retrieval
  4. 2024MLLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training
  5. 2026MMAgent-R2: Learning to Rerank and Reject for Agentic mRAG
  6. 2026KIRA: Knowledge-Intensive Image Retrieval and Reasoning Architecture for Specialized Visual Domains
  7. 2025Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts
  8. 2026Rethinking the Reranker: Boundary-Aware Evidence Selection for Robust Retrieval-Augmented Generation