新方法通过自适应检索和智能恢复增强视觉文档问答

报道来源 [6]

1. arXiv cs.AI TIER_1 English(EN) · Adrien Mialland, Marc Plantevit, Julien Gallois, C\'eline Robardet · 2026-09-03 04:00

   ViSAR：用于视觉文档问答的无训练自适应-$k$检索

   arXiv:2609.02486v1 Announce Type: cross Abstract: Document Visual Question Answering (DocVQA) often leverages Retrieval-Augmented Generation (RAG), where late-interaction encoders are commonly used to identify document pages relevant to a user query, before answer generation by a…

2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Céline Robardet · 2026-09-02 11:54

   ViSAR：视觉文档问答的无训练自适应-$k$检索

   Document Visual Question Answering (DocVQA) often leverages Retrieval-Augmented Generation (RAG), where late-interaction encoders are commonly used to identify document pages relevant to a user query, before answer generation by a Large Vision-Language Model (LVLM). Existing appr…

3. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Céline Robardet · 2026-09-02 11:54

   ViSAR：视觉文档问答的无训练自适应-$k$检索

   Document Visual Question Answering (DocVQA) often leverages Retrieval-Augmented Generation (RAG), where late-interaction encoders are commonly used to identify document pages relevant to a user query, before answer generation by a Large Vision-Language Model (LVLM). Existing appr…

4. arXiv cs.AI TIER_1 English(EN) · Zihan Huang, Shihang Wu, Junle Liu, Peirong Zhang, Yongxin Shi, Xuhan Zheng, Lianwen Jin · 2026-09-01 04:00

   DocIntent：面向真实世界文档视觉问答的基于可回答性的代理恢复

   arXiv:2608.29037v1 Announce Type: cross Abstract: Real-world degradations such as blur, shadow, distortion, and moire patterns severely impair the document question-answering capabilities of Multimodal Large Language Models (MLLMs). Applying restoration tools before Visual Questi…

5. Hugging Face Daily Papers TIER_1 English(EN) · 2026-08-19 00:00

   超越视觉相似性：面向知识型视觉问答的实体对齐检索

   KBMR uses a multimodal language model to embed images by semantic identity rather than surface appearance, improving retrieval and visual question answering via continuous distillation and hard negative sampling.

6. arXiv cs.CV TIER_1 English(EN) · Zhuohong Chen, Zhenxian Wu, Yunyao Yu, Hangrui Xu, Zirui Liao, Zhifang Liu, Xiangwen Deng, Pen Jiao, Haoqian Wang · 2026-09-01 04:00

   学习搜索：一种基于决策的知识驱动视觉问答代理

   arXiv:2604.07146v3 Announce Type: replace Abstract: Knowledge-based visual question answering (KB-VQA) requires vision-language models to understand images and use external knowledge, especially for rare entities and long-tail facts. Most existing retrieval-augmented generation (…