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arXiv · 2608.25434

DocPC: Document-Level Visual Retrieval via Representative Page Composition

Chengsong You·Qiyi Jiang·Junwei Zhou·Xiaoyu Cao·Weiyao Wang·Yiwei Xu·Ziyan Zhao·Zhen Sun·Qicheng Zhu·Xuanyi Fu·Yufan Chen·Yilun Li·Rongkang Xiong·Yunhai Hu·Nan Du
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Abstract

Visual document retrieval has advanced by encoding page screenshots with vision-language models, bypassing OCR pipelines. However, existing methods remain page-centric, misaligned with real-world scenarios requiring complete document retrieval. A naive page-then-document aggregation suffers from linear indexing cost and degraded retrieval when relevance spans multiple pages. We propose DocPC, a document-level visual retrieval framework based on Representative Page Composition: selecting representative pages and composing them into a single grid image for document-level indexing, reducing indexed images, vectors, and storage by 10.1x and end-to-end indexing time by roughly 7.7x. To handle multi-positive supervision prevalent at the document level, we combine multi-positive contrastive learning with sparsely scheduled listwise optimization. We also introduce DocViRe, a benchmark with multi-positive relevance annotations. DocPC-ColQwen achieves NDCG@5 of 44.09 on DocViRe, outperforming the strongest page-level baseline at 38.91 while reducing storage by 10.1x. Code is available at https://anonymous.4open.science/r/DocPC-Document-Level-Visual-Retrieval-via-Representative-Page-Composition-1D52. Data is available at https://huggingface.co/datasets/anonymous-7219/docpc.

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Chengsong You, Qiyi Jiang, Junwei Zhou, Xiaoyu Cao, Weiyao Wang, Yiwei Xu, Ziyan Zhao, Zhen Sun, Qicheng Zhu, Xuanyi Fu, Yufan Chen, Yilun Li, Rongkang Xiong, Yunhai Hu, Nan Du. 2026-08-28. DocPC: Document-Level Visual Retrieval via Representative Page Composition. https://arxiv.org/abs/2608.25434

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