aiqbench analysis
R3G introduces a modular framework that enhances visual question answering by decoupling reasoning from image retrieval and reranking. By generating a reasoning plan to identify necessary visual cues before executing a two-stage selection process, the method achieves state-of-the-art accuracy across multiple MLLM backbones. The release of open-source code and data provides a practical foundation for improving multimodal models' ability to integrate visual evidence effectively.
R3G: A Reasoning-Retrieval-Reranking Framework for Vision-Centric Answer Generation
arXiv:2602.00104v3 Announce Type: replace-cross Abstract: Vision-centric retrieval for VQA requires retrieving images to supply missing visual cues and integrating them into the reasoning process. However, selecting the right images and integrating them effectively into the model's reasoning remains challenging.To address this challenge, we propose R3G, a modular Reasoning-Retrieval-Reranking framework.It first produces a brief reasoning plan that specifies the required visual cues, then adopts a two-stage strategy, with coarse retrieval followed by fine-grained reranking, to select evidence images.On MRAG-Bench, R3G improves accuracy across six MLLM backbones and nine sub-scenarios, achieving state-of-the-art overall performance. Ablations show that sufficiency-aware reranking and reasoning steps are complementary, helping the model both choose the right images and use them well. We release code and data at https://github.com/czh24/R3G.
Abstract:Vision-centric retrieval for VQA requires retrieving images to supply missing visual cues and integrating them into the reasoning process. However, selecting the right images and integrating them effectively into the model's reasoning remains this http URL address this challenge, we propose R3G, a modular Reasoning-Retrieval-Reranking this http URL first produces a brief reasoning plan that specifies the required visual cues, then adopts a two-stage strategy, with coarse retrieval followed by fine-grained reranking, to select evidence this http URL MRAG-Bench, R3G improves accuracy across six MLLM backbones and nine sub-scenarios, achieving state-of-the-art overall performance. Ablations show that sufficiency-aware reranking and reasoning steps are complementary, helping the model both choose the right images and use them well. We release code and data at this https URL.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2602.00104 [cs.CV]
(or arXiv:2602.00104v3 [cs.CV] for this version)
Full text under the publisher's open licence · 141 words
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