arXiv Computer Vision October 16, 2026 By Yongbin Choi, Yongwoo Song, Mujeen Sung

KoViDoRe: A Benchmark for Korean Visual Document Retrieval

Read the original on arXiv Computer Vision →

KoViDoRe is a new benchmark for Korean visual document retrieval that addresses gaps in existing English‑centric datasets by including Korean documents with complex layouts such as tables, figures, and multi‑column structures. The dataset is built through a multi‑stage pipeline involving structured document parsing, synthetic query generation (summary‑based and context‑based), and human‑verified relevance mapping. Evaluation on KoViDoRe shows that current multimodal retrieval models struggle with Korean documents, especially when handling structured content and diverse query types, prompting the creation of a large‑scale training set, Ko‑VDR Train Public, to support model development.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

arXiv Computer Vision

Aug 24

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. Howe...

By Yongbin Choi, Yongwoo Song, Mujeen Sung

multimodal benchmarks

arXiv Computation and Language

2d ago

Cascaded Retrieval with Cross-Modal Query Refinement for Efficient Visual Document Search

arXiv:2610.20367v1 Announce Type: new Abstract: Retrieving information from visually rich documents requires matching queries against pages that contain complex evidence in tables, figures, and layou...

By Diana Turmakhan, Aseem Srivastava, Vivek Gupta, Iryna Gurevych, Thamar Solorio

multimodal benchmarks

arXiv Computation and Language

Aug 25

L\"etzCross: A Cross-Lingual Page-Level Benchmark for Multimodal Retrieval over Luxembourgish Documents

arXiv:2608.21714v1 Announce Type: new Abstract: Recent page-image retrievers such as ColPali have improved retrieval over visually rich documents, yet little is known about how they behave in cross-l...

By Omar El Bachyr, Fred Philippy, Laura Maria Bernardy, Saad Ezzini, Jacques Klein, Tegawende Bissyande

rag computer-vision fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 24

Multimodal CoLRAG-TF: Triple-Filtered Retrieval for Complex PDFs

arXiv:2607. 20517v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) over heterogeneous PDF collections remains challenging due to multimodal content, domain-specific terminology, and the need for multi-hop reasoning across dispersed evidence.

By Takato Yasuno

llms rag computer-vision multimodal benchmarks safety

Hugging Face Trending Papers

Aug 2

VaRS-Doc: Interpretation-Aware Variant Representations via Latent Self-Probing for Visual Document Retrieval

Visual document retrieval has recently become increasingly important in applications such as enterprise search, scientific literature discovery, and retrieval-augmented generation. These applications depend on efficiently identifying query-relevant pages across large collections of visually rich documents.

rag benchmarks

arXiv AI

Sep 2

MIDR: Enrichment-Augmented Indexing for Multimodal Document Retrieval

MIDR (Multimodal Indexing for Document Retrieval) is a training‑free framework that enriches document indexes by converting rendered pages into verified textual fields with a multimodal LLM, then indexing those fields with BM25F and optionally fusing with dense retrieval. By shifting multimodal reasoning to index time, MIDR enables text‑centric serving while retaining multimodal evidence, achieving a 23.0% relative gain over BM25 on ViDoRe V3 and outperforming ColQwen2.5 on several domains with significantly smaller index memory and lower query latency.

By Debanjan Mahata, Atharva Tendle, Daniel Preotiuc-Pietro, Yong Zhuang, Ozan Irsoy

llms computer-vision multimodal