超越网格：基于解析视觉文档表示的布局感知多向量检索
Beyond the Grid: Layout-Informed Multi-Vector Retrieval with Parsed Visual Document Representations
- The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））
- Alibaba Cloud Computing（阿里云计算）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
ColParse通过生成布局感知的子图像嵌入和全局向量融合，实现高效多向量检索，显著降低存储需求并提升性能。
AI中文摘要：
利用富含视觉信息的文档的全部潜力需要能够理解不仅仅是文本，还包括复杂布局的检索系统，这是视觉文档检索（VDR）中的核心挑战。现有的多向量架构虽然强大，但面临一个关键的存储瓶颈，当前的优化策略，如嵌入合并、剪枝或使用抽象令牌，无法在不损害性能或忽略关键布局线索的情况下解决。为了解决这个问题，我们引入了ColParse，一种新的范式，利用文档解析模型生成一组小的布局感知子图像嵌入，然后与全局页面级向量融合，创建一个紧凑且结构感知的多向量表示。广泛的实验表明，我们的方法将存储需求减少了超过95%，同时在众多基准和基础模型上带来了显著的性能提升。ColParse因此在多向量检索的细粒度精度和大规模部署的实践需求之间建立了关键桥梁，为高效且可解释的多模态信息系统提供了新的途径。
英文摘要：
Harnessing the full potential of visually-rich documents requires retrieval systems that understand not just text, but intricate layouts, a core challenge in Visual Document Retrieval (VDR). The prevailing multi-vector architectures, while powerful, face a crucial storage bottleneck that current optimization strategies, such as embedding merging, pruning, or using abstract tokens, fail to resolve without compromising performance or ignoring vital layout cues. To address this, we introduce ColParse, a novel paradigm that leverages a document parsing model to generate a small set of layout-informed sub-image embeddings, which are then fused with a global page-level vector to create a compact and structurally-aware multi-vector representation. Extensive experiments demonstrate that our method reduces storage requirements by over 95% while simultaneously yielding significant performance gains across numerous benchmarks and base models. ColParse thus bridges the critical gap between the fine-grained accuracy of multi-vector retrieval and the practical demands of large-scale deployment, offering a new path towards efficient and interpretable multimodal information systems.
