arXiv cs.CL
TIER_1English(EN)·Erfan Loweimi, Mengjie Qian, Kate Knill, Guanfeng Wu, Chi-Ho Chan, Abbas Haider, Muhammad Awan, Josef Kittler, Hui Wang, Mark Gales·
arXiv:2606.05931v1 Announce Type: new Abstract: When retrieving a person from a video archive by voice and face, should the system be multimodal or not? In real-world broadcast archives, unlike curated benchmarks, a target may be heard but unseen, seen but unheard, or both. Fusin…
When retrieving a person from a video archive by voice and face, should the system be multimodal or not? In real-world broadcast archives, unlike curated benchmarks, a target may be heard but unseen, seen but unheard, or both. Fusing scores from an absent modality injects noise, …
arXiv:2606.04240v1 Announce Type: cross Abstract: Retrieval over visually-rich documents, pages that interleave text with figures, tables, and charts, is essential for multimodal retrieval-augmented generation, yet most retrievers still discard the visual channel. The \emph{Multi…
arXiv cs.AI
TIER_1English(EN)·Alicja Dobrzeniecka, Filip Szatkowski, Sebastian Cygert, Szymon Lukasik, Bartlomiej Twardowski·
arXiv:2605.31229v1 Announce Type: cross Abstract: While retrieval is a core function of vision-language models, continually updating these models for retrieval tasks remains critically underexplored. Existing work often approaches continual retrieval through the lens of class-inc…
While retrieval is a core function of vision-language models, continually updating these models for retrieval tasks remains critically underexplored. Existing work often approaches continual retrieval through the lens of class-incremental learning (CIL), evaluating both standard …
As large-scale visual-document corpora such as arXiv papers and enterprise PDFs continue to grow, visual-document retrieval has gained increasing attention; yet it still lacks a deployable system that lexically indexes visual documents to serve queries without neural encoding at …
arXiv cs.AI
TIER_1English(EN)·Joongmin Shin, Gyuho Shim, Jeongbae Park, Jaehyung Seo, Heuiseok Lim·
arXiv:2605.29606v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) for document-based Open-domain Question Answering (ODQA) on large-scale industrial corpora faces two critical bottlenecks: routing failure in locating the correct document and evidence fragmentat…
Multimodal documents contain diverse elements, such as tables, figures, and layouts, which can complicate retrieval tasks. While current approaches typically combine dense visual embedding models with supervised rerankers to achieve high-precision retrieval, they face inherent li…
Retrieval-augmented generation (RAG) for document-based Open-domain Question Answering (ODQA) on large-scale industrial corpora faces two critical bottlenecks: routing failure in locating the correct document and evidence fragmentation in integrating scattered information. Existi…
Item-to-Item (I2I) retrieval is a fundamental part of modern content platforms, supporting critical industrial workflows from recommendation engines to content auditing. While multimodal embedding methods have advanced general retrieval, they often falter in I2I scenarios due to …
arXiv:2507.08064v3 Announce Type: replace-cross Abstract: As multimedia content expands, the demand for unified multimodal retrieval (UMR) in real-world applications increases. Recent work leverages multimodal large language models (MLLMs) to tackle this task. However, their larg…
arXiv cs.CV
TIER_1English(EN)·Prasanna Sridhar, Horace Lee, David M. S. Pinto, Andrew Zisserman, Abhishek Dutta·
arXiv:2602.12819v2 Announce Type: replace-cross Abstract: In this paper, we present WISE, an open-source audiovisual search engine which integrates a range of multimodal retrieval capabilities into a single, practical tool accessible to users without machine learning expertise. W…
arXiv cs.CV
TIER_1English(EN)·Gyu-Hwung Cho (NAVER Corp., Republic of Korea, Seoul National University, Republic of Korea), Youngjune Lee (NAVER Corp., Republic of Korea), Kiyoon Jeong (NAVER Corp., Republic of Korea), Siyoung Lee (NAVER Corp., Republic of Korea), Sanggyu Han (NAVER …·
arXiv:2605.30917v1 Announce Type: cross Abstract: As large-scale visual-document corpora such as arXiv papers and enterprise PDFs continue to grow, visual-document retrieval has gained increasing attention; yet it still lacks a deployable system that lexically indexes visual docu…
arXiv cs.CV
TIER_1English(EN)·Chenlong Deng, Mengjie Deng, Junjie Wu, Dun Zeng, Teng Wang, Qingsong Xie, Jiadeng Huang, Shengjie Ma, Changwang Zhang, Zhaoxiang Wang, Jun Wang, Yutao Zhu, Zhicheng Dou·
arXiv:2602.10809v2 Announce Type: replace Abstract: Existing multimodal retrieval systems excel at semantic matching but implicitly assume that query-image relevance can be measured in isolation. This paradigm overlooks the rich dependencies inherent in realistic visual streams, …
arXiv cs.CV
TIER_1English(EN)·Ruofan Hu, Menghui Zhu, Jieming Zhu, Bo Chen, Shengyang Xu, Minjie Hong, Xiaoda Yang, Sashuai Zhou, Li Tang, Tao Jin, Zhou Zhao·
arXiv:2605.30027v1 Announce Type: new Abstract: Multimodal documents contain diverse elements, such as tables, figures, and layouts, which can complicate retrieval tasks. While current approaches typically combine dense visual embedding models with supervised rerankers to achieve…
