arXiv:2606.23881v1 Announce Type: new Abstract: Knowledge-Based Visual Question Answering (KB-VQA) requires grounding visual queries to external knowledge beyond directly observable content in images. While recent multi modal large language models (MLLMs) show strong perceptual abilities, they struggle on KB-VQA tasks requiring groundings from both fine-grained entity and evidence levels. Most existing multi-modal retrieval augmented generation (MM-RAG) methods tightly couple entity discrimination and section-level evidence ranking into a single re-ranking stage, leading to high cost and limit
The paper leverages recent advancements in understanding how MLLMs process external knowledge and visual queries, addressing existing limitations in KB-VQA tasks.
Improving Knowledge-Based Visual Question Answering (KB-VQA) directly enhances the practical utility and reliability of AI systems in complex, real-world reasoning tasks that require integrating visual and external knowledge.
This research outlines a more efficient and less costly approach to integrating entity identification and evidence ranking in VQA, potentially improving the performance and scalability of knowledge-based AI applications.
- · AI researchers
- · developers of MLLMs
- · industries requiring visual reasoning
- · incumbent complex MM-RAG methods
Improved performance in VQA tasks requiring external knowledge.
Accelerated development of more robust AI agents capable of nuanced visual and semantic understanding.
Enhanced trust and broader adoption of AI systems in applications needing high-fidelity information retrieval and reasoning from visual inputs.
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Read at arXiv cs.CL