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 i
The rapid advancement in multimodal AI and the increasing complexity of VQA tasks necessitate more sophisticated frameworks for integrating visual retrieval effectively.
This framework significantly improves the accuracy and reliability of vision-centric AI systems by enhancing their ability to retrieve and integrate relevant visual cues for reasoning.
Vision-centric AI models can now produce more accurate and contextually relevant answers by employing a structured reasoning-retrieval-reranking process.
- · AI developers
- · Multimodal AI applications
- · Generative AI
- · Computer vision researchers
- · Less sophisticated VQA models
- · AI systems relying on simple retrieval methods
Improved performance in complex VQA tasks, leading to more reliable AI outputs.
Accelerated development of AI agents capable of nuanced visual understanding and interaction.
Enhanced AI capabilities contribute to broader commercial applications requiring sophisticated visual reasoning, potentially impacting white-collar workflows.
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Read at arXiv cs.AI