English(EN) Rethinking LLM-Guided Visual Question Answering: Does Deeper Knowledge Integration Really Help?

LLM知识整合深度影响VQA性能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员探讨了如何将大型语言模型（LLM）的知识最佳地整合到视觉问答（VQA）系统中。他们发现在OK-VQA基准测试中，较浅的整合，特别是后期融合，比注意力矩阵注入等更深层次的方法表现更好。浅层方法实现了39.4%的精确匹配得分，显著优于未引导基线和得分31.8%的深层整合方法。这表明LLM知识整合的最佳深度取决于可用数据。 AI

影响研究结果表明，对于VQA系统而言，较浅的LLM整合可能更有效，有可能在数据稀缺的情况下优化资源利用和性能。

排序理由该集群包含一篇学术论文，详细介绍了LLM引导的VQA的新研究发现。[lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 该集群包含一篇学术论文，详细介绍了LLM引导的VQA的新研究发现。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Aneerban Saha, Vijay Kumar, Sameer Madhav D., Priyam Srivastava, Dhruv Bansal · 2026-10-23 04:00

Rethinking LLM-Guided Visual Question Answering: Does Deeper Knowledge Integration Really Help?

arXiv:2610.17079v2 Announce Type: replace Abstract: Some questions about an image need commonsense or factual knowledge that its pixels do not contain. Large Language Models (LLMs) can supply this knowledge to Visual Question Answering (VQA) systems, but the depth at which their …

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Rethinking LLM-Guided Visual Question Answering: Does Deeper Knowledge Integration Really Help?

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