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
ビジュアル文書の理解は、視覚的に豊かな文書から知識を獲得するために不可欠であり、研究の関心が高まっています。既存の調査は、建築物や技術的なアプローチを強調し、タスクボトルが技術的な選択と研究の優先事項を形成する方法に限られた注意を払います。私たちは、長文書のビジュアル質問回答(VQA)を4つのコンポーネントに分解するタスク中心の調査を提示します:文書の解析、証拠の組織と回収、ジェネレーターの組織と学習、および評価。
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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