DocPC: Document-Level Visual Retrieval via Representative Page Composition

[2608.25434] DocPC: Document-Level Visual Retrieval via Representative Page Composition Skip to main content Search Submit Donate Log in Computer Science > Information Retrieval arXiv:2608.25434 (cs) [Submitted on 26 Aug 2026 ( v1 ), last revised 28 Aug 2026 (this version, v3)] Title: DocPC: Document-Level Visual Retrieval via Representative Page Composition Authors: 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 View PDF HTML (experimental) 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 this https URL . Data is available at this https URL . Comments: 15 pages, 5 figures, 8 tables Subjects: Information Retrieval (cs.IR) Cite as: arXiv:2608.25434 [cs.IR] (or arXiv:2608.25434v3 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2608.25434 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Chengsong You [ view email ] [v1] Wed, 26 Aug 2026 06:46:44 UTC (643 KB) [v2] Thu, 27 Aug 2026 08:37:38 UTC (644 KB) [v3] Fri, 28 Aug 2026 07:33:35 UTC (644 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.IR < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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