Yufei Tong, Guanjie Cheng, Tianle Wu, Leilei Zhang, Hang Zheng, Peihan Wu, Jianwei Yin, Shuiguang Deng · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31385 · open-access PDF
视觉文档的理解对于从视觉丰富的文档中获取知识至关重要,并且正在吸引越来越多的研究兴趣。现有调查强调架构或技术方法,对任务瓶颈如何塑造技术选择和研究优先事项的关注有限。我们提出了一个以任务为中心的调查,将长文档视觉问答(VQA)分解为四个组件:文档分析,证据组织和检索,发电机组织和学习以及评估。
Machine translation (Meta M2M100); the English original follows.
Visual document understanding is essential for acquiring knowledge from visually rich documents and is attracting growing research interest. Existing surveys emphasize architectures or technical approaches, with limited attention to how task bottlenecks shape technical choices and research priorities. We present a task-centric survey that decomposes long-document visual question answering (VQA) into four components: document parsing, evidence organization and retrieval, generator organization and learning, and evaluation. For each, we develop a fine-grained taxonomy and trace motivations, tech
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