通过动态剪枝文档嵌入，大幅降低视觉文档检索的存储开销。
DocPruner: A Storage-Efficient Framework for Multi-Vector Visual Document Retrieval via Adaptive Patch-Level Embedding Pruning
- 基于文档内补丁注意力分布，自适应识别并删除冗余嵌入
- 存储量减少50%-60%，检索性能几乎无损
- 适用于大规模视觉文档检索系统部署
视觉文档检索（VDR）是利用结合视觉与文本线索的查询来检索富含视觉信息的文档页面，对众多实际应用至关重要。近期先进方法采用大视觉语言模型（LVLMs）的多向量范式，将每个文档表示为补丁级嵌入以捕捉细粒度信息。尽管效果优异，该方法带来显著存储负担：每页需存储数百个向量，导致大规模部署成本高昂且不切实际。为此，我们提出DocPruner，首个在VDR中采用自适应补丁级嵌入剪枝的框架，有效降低存储开销。DocPruner利用文档内补丁间的注意力分布，动态识别并丢弃冗余嵌入。该自适应机制使主流多向量VDR模型的存储量减少50%-60%，同时保持检索性能几乎不变。在十余个代表性数据集上的大量实验表明，DocPruner为构建高效、灵活、可扩展的存储优化型大规模VDR系统提供了可靠方案。
原文摘要 · Abstract (English)
Visual Document Retrieval (VDR), the task of retrieving visually-rich document pages using queries that combine visual and textual cues, is crucial for numerous real-world applications. Recent state-of-the-art methods leverage Large Vision-Language Models (LVLMs) in a multi-vector paradigm, representing each document as patch-level embeddings to capture fine-grained details. While highly effective, this approach introduces a critical challenge: prohibitive storage overhead, as storing hundreds of vectors per page makes large-scale deployment costly and impractical. To address this, we introduce DocPruner, the first framework to employ adaptive patch-level embedding pruning for VDR to effectively reduce the storage overhead. DocPruner leverages the intra-document patch attention distribution to dynamically identify and discard redundant embeddings for each document. This adaptive mechanism enables a significant 50-60% reduction in storage for leading multi-vector VDR models with negligible degradation in document retrieval performance. Extensive experiments across more than ten representative datasets validate that DocPruner offers a robust, flexible, and effective solution for building storage-efficient, large-scale VDR systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。