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Multimodal Iterative RAG for Knowledge-Intensive Visual Question Answering

Created by
  • Haebom

Author

Changin Choi, Wonseok Lee, Jungmin Ko, Wonjong Rhee

Outline

This paper aims to improve the performance of knowledge-intensive visual question answering (VQA) using multimodal large-scale language models (MLLMs). To overcome the limitations of conventional single-pass retrieval augmented generation (RAG) methods, we propose a multimodal iterative RAG framework (MI-RAG), which leverages inference to improve retrieval and integrates knowledge synthesis. MI-RAG iteratively generates multiple queries, retrieves diverse knowledge, and synthesizes them to deepen understanding. Benchmark experiments on Encyclopedic VQA, InfoSeek, and OK-VQA demonstrate that MI-RAG significantly improves both retrieval and response accuracy.

Takeaways, Limitations

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Takeaways:
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A novel approach to solving knowledge-intensive VQA problems (MI-RAG framework).
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Improve model comprehension through iterative inference and knowledge synthesis.
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Demonstrated improved performance compared to existing models in various benchmarks.
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Building a scalable framework for knowledge-intensive VQA.
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Limitations:
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Further explanation of the specific framework implementation and computational complexity is needed.
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Further research is needed to determine the generalizability of MI-RAG and its applicability to other multimodal problems.
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Absence of specifics on knowledge base selection and management strategies.
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