Rethinking LLM-Guided Visual Question Answering: Does Deeper Knowledge Integration Really Help?
Knowledge-based Visual Question Answering (VQA) requires reasoning that extends beyond visible image content, demanding external commonsense and factual knowledge. While recent work leverages Large Language Models (LLMs) as knowledge sources, the question of how deeply such guidance should be integrated into a vision language architecture remains underexplored. In this work, we generate structured reasoning guidance, visual focus concepts, distractor elements and external knowledge using Llama~3.3~70B and systematically compare two integration strategies within a unified frozen ViT + FLAN-T5 framework on the OK-VQA benchmark: decision level late fusion and attention-matrix injection. Cont...
| Authors | Aneerban Saha, Vijay Kumar, Sameer Madhav D. et al. |
|---|---|
| Primary category | cs.CV |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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