系统梳理多模态文档检索新范式，助力精准获取视觉文档信息
Unlocking Multimodal Document Intelligence: From Current Triumphs to Future Frontiers of Visual Document Retrieval
- 基于多模态大模型视角，分类梳理文档检索方法
- 提出三类核心方法：多模态嵌入、重排序模型与RAG/智能体集成
- 适合关注文档智能与多模态大模型的科研与工程人员
随着多模态信息的快速普及，视觉文档检索（VDR）已成为连接非结构化视觉数据与精准信息获取的关键前沿。与传统自然图像检索不同，视觉文档具有密集文本内容、复杂布局和细粒度语义依赖等独特特征。本文首次全面综述了多模态大模型（MLLM）时代下的VDR研究格局，从基准数据集出发，系统分析方法演进，将主流方法分为三类：多模态嵌入模型、多模态重排序模型，以及检索增强生成（RAG）与智能体系统在复杂文档智能中的融合应用。最后，识别现存挑战并展望未来方向，为多模态文档智能提供清晰发展路径。
原文摘要 · Abstract (English)
With the rapid proliferation of multimodal information, Visual Document Retrieval (VDR) has emerged as a critical frontier in bridging the gap between unstructured visually rich data and precise information acquisition. Unlike traditional natural image retrieval, visual documents exhibit unique characteristics defined by dense textual content, intricate layouts, and fine-grained semantic dependencies. This paper presents the first comprehensive survey of the VDR landscape, specifically through the lens of the Multimodal Large Language Model (MLLM) era. We begin by examining the benchmark landscape, and subsequently dive into the methodological evolution, categorizing approaches into three primary aspects: multimodal embedding models, multimodal reranker models, and the integration of Retrieval-Augmented Generation (RAG) and Agentic systems for complex document intelligence. Finally, we identify persistent challenges and outline promising future directions, aiming to provide a clear roadmap for future multimodal document intelligence.
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