arXiv：cs.CL· Diana Turmakhan, Aseem Srivastava, Vivek Gupta, Iryna Gurevych, Thamar Solorio·· 16 小时前
SCOUT：级联检索与跨模态查询精炼实现高效视觉文档搜索
Cascaded Retrieval with Cross-Modal Query Refinement for Efficient Visual Document Search
AI 导读
SCOUT 是一种级联双轨检索框架，视觉轨先用轻量父检索器将语料过滤为候选池、再对候选应用更强的多向量重排器，语义轨则基于离线生成的层级页面与文档摘要提供互补信号，二者通过跨模态查询精炼融合。在 4 个基准上对比 6 个基线，查询时延较最佳基线最高提升 80.7 倍（平均 28.8 倍），检索性能在所有基准上最高超出 SOTA 7.8% NDCG。
正文
Abstract:Retrieving information from visually rich documents requires matching queries against pages that contain complex evidence in tables, figures, and layout-dependent structures. Current vision-language retrievers address this but incur query-time costs that grow with corpus size and are impractical to scale. We propose SCOUT, a cascaded two-track retrieval framework that separates retrieval performance from exhaustive search. The visual track first uses a lightweight parent retriever to filter the corpus to a compact candidate pool, then applies a stronger multi-vector reranker only to those candidates. In parallel, a semantic track retrieves over hierarchical page and document summaries generated offline to provide a signal complementary to visual matching. The outputs are fused through cross-modal query refinement, which uses semantic scores to adjust the visual query representation before final ranking. We evaluate SCOUT on 4 major benchmarks against 6 baselines and observe a significant query-time latency improvement up to 80.7x (28.8x on average) relative to the best baseline, while exceeding state-of-the-art retrieval performance by up to 7.8% NDCG across all benchmarks.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.20367 [cs.CL] |
| (or arXiv:2610.20367v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.20367 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Diana Turmakhan [view email]
[v1]
Wed, 14 Oct 2026 15:40:27 UTC (1,637 KB)
来源：arXiv：cs.CL · arxiv.org