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New benchmarks and methods advance visual document retrieval for complex queries · 4 sources tracked

By PulseAugur Editorial · [6 sources] ·

Researchers have developed new frameworks for visual document retrieval, addressing limitations in existing page-centric approaches. DocPC, for instance, uses representative page composition to create a single grid image for document-level indexing, significantly reducing storage and indexing time while improving retrieval accuracy. Separately, KoViDoRe introduces a benchmark and training dataset specifically for Korean visual documents, highlighting current models' struggles with complex structures and multi-page relevance. VisDocAgentBench further explores agentic retrieval for visually rich documents, demonstrating that while static retrievers falter on complex queries, agents can recover performance, especially when combined with visual retrieval capabilities. AI

IMPACT Advances in visual document retrieval could improve information access from complex documents and support agentic search capabilities.

RANK_REASON Multiple research papers introducing new benchmarks and methods for visual document retrieval.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 6 sources. How we write summaries →

New benchmarks and methods advance visual document retrieval for complex queries · 4 sources tracked

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COVERAGE [6]

  1. arXiv cs.IR (Information Retrieval) TIER_1 Italiano(IT) · Nan Du ·

    DocPC: Document-Level Visual Retrieval via Representative Page Composition

    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 aggregat…

  2. arXiv cs.IR (Information Retrieval) TIER_1 Italiano(IT) · Nan Du ·

    DocPC: Document-Level Visual Retrieval via Representative Page Composition

    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 aggregat…

  3. arXiv cs.IR (Information Retrieval) TIER_1 Română(RO) · Mujeen Sung ·

    KoViDoRe: Korean Visual Document Retrieval

    Recent advances in multimodal retrieval have improved the ability to retrieve information from visually rich documents such as PDFs and reports. However, existing benchmarks remain largely centered on English and provide limited coverage of Korean visual documents with complex st…

  4. arXiv cs.CV TIER_1 English(EN) · Chanho Park, Daehyeon Choi, Jihyun Lee, Minhyuk Sung ·

    Retrieval Heads Meet Vision: Uncovering How VLMs Locate and Extract Visual Information

    arXiv:2608.27417v1 Announce Type: new Abstract: Vision-language models (VLMs) can locate an image region referred to by a text prompt and route the corresponding visual evidence to the output, yet the internal mechanism behind this behavior is not understood. Inspired by retrieva…

  5. arXiv cs.CV TIER_1 English(EN) · Lexiang Hu, Yanzhao Zhang, Mingxin Li, Dingkun Long, Yikang Li, Fuwei Zhang, Yisen Wang, Zhouchen Lin ·

    VisDocAgentBench: Benchmarking Agents for Visually Rich Document Retrieval

    arXiv:2608.17889v1 Announce Type: cross Abstract: Visually rich documents encode relevance through language, layout, structured visual elements, and corpus context, yet retrieval is typically evaluated by one-shot query--page matching. Agentic-search benchmarks usually score down…

  6. arXiv cs.CV TIER_1 Română(RO) · Yongbin Choi, Yongwoo Song, Mujeen Sung ·

    KoViDoRe: Korean Visual Document Retrieval

    arXiv:2608.20840v1 Announce Type: cross Abstract: Recent advances in multimodal retrieval have improved the ability to retrieve information from visually rich documents such as PDFs and reports. However, existing benchmarks remain largely centered on English and provide limited c…

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