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New benchmark KoViDoRe aims to improve Korean visual document retrieval

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced KoViDoRe, a new benchmark designed to improve Korean visual document retrieval. This benchmark addresses the limitations of existing English-centric datasets by including Korean documents with complex layouts such as tables and multi-column structures. The project also includes a large-scale training dataset, Ko-VDR Train Public, to aid in developing specialized retrieval models, as current multimodal models show significant struggles with this task. AI

IMPACT Aims to improve multimodal retrieval capabilities for non-English languages and complex document structures.

RANK_REASON The cluster describes a new benchmark and training dataset for a specific research area (Korean visual document retrieval), published on arXiv.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New benchmark KoViDoRe aims to improve Korean visual document retrieval

COVERAGE [2]

  1. 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…

  2. 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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