Fine-Grained Retrieval-Augmented Generation for Visual Question Answering

• URL: http://arxiv.org/abs/2502.20964v2
• Date: Fri, 11 Apr 2025 16:02:25 GMT
• Title: Fine-Grained Retrieval-Augmented Generation for Visual Question Answering
• Authors: Zhengxuan Zhang, Yin Wu, Yuyu Luo, Nan Tang,
• Abstract summary: Visual Question Answering (VQA) focuses on providing answers to natural language questions by utilizing information from images.<n>Retrieval-augmented generation (RAG) leveraging external knowledge bases (KBs) emerges as a promising approach.<n>This study presents fine-grained knowledge units, which merge textual snippets with entity images stored in vector databases.
• Score: 12.622529359686016
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Visual Question Answering (VQA) focuses on providing answers to natural language questions by utilizing information from images. Although cutting-edge multimodal large language models (MLLMs) such as GPT-4o achieve strong performance on VQA tasks, they frequently fall short in accessing domain-specific or the latest knowledge. To mitigate this issue, retrieval-augmented generation (RAG) leveraging external knowledge bases (KBs), referred to as KB-VQA, emerges as a promising approach. Nevertheless, conventional unimodal retrieval techniques, which translate images into textual descriptions, often result in the loss of critical visual details. This study presents fine-grained knowledge units, which merge textual snippets with entity images stored in vector databases. Furthermore, we introduce a knowledge unit retrieval-augmented generation framework (KU-RAG) that integrates fine-grained retrieval with MLLMs. The proposed KU-RAG framework ensures precise retrieval of relevant knowledge and enhances reasoning capabilities through a knowledge correction chain. Experimental findings demonstrate that our approach significantly boosts the performance of leading KB-VQA methods, achieving an average improvement of approximately 3% and up to 11% in the best case.

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