Visual Document Retrieval Goes Multilingual

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...

A Deepdive into Aya Vision: Advancing the Frontier of Multilingual Multimodality

SEA-CLIP-Tiny: Efficient Multilingual Text-Vision Embedding for Southeast Asian Languages

SEA-CLIP-Tiny is a compact multilingual text‑vision embedding model designed for Southeast Asian languages, containing fewer than 50 million parameters. It adapts a CLIP‑KD framework with region‑specific data curation and multilingual teacher guidance. Across seven languages, it outperforms other student models, achieving R@1 = 12.9%, R@5 = 31.5%, and R@10 = 42.2%, and surpasses MobileCLIP2 by 12.1 points in R@10 while using 38.4% fewer parameters and lower CPU latency.

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...

Granite Embedding Multilingual R2: Open Apache 2.0 Multilingual Embeddings with 32K Context — Best Sub-100M Retrieval Quality

Evaluating Perspectival Biases in Cross-Modal Retrieval

arXiv:2510.26861v4 Announce Type: replace-cross Abstract: Multimodal retrieval systems are expected to operate in a semantic space, agnostic to the language or cultural origin of the query. In practi...