Détail du document
Identifiant
oai:arXiv.org:2410.11619
Sujet
Computer Science - Computer Vision... Computer Science - Computation and...Auteur
Kriz, Reno Sanders, Kate Etter, David Murray, Kenton Carpenter, Cameron Van Ochten, Kelly Recknor, Hannah Guallar-Blasco, Jimena Martin, Alexander Colaianni, Ronald King, Nolan Yang, Eugene Van Durme, BenjaminCatégorie
Computer Science
Année
2024
Date de référencement
12/02/2025
Mots clés
computer videos multimodal queries videoRésumé
Efficiently retrieving and synthesizing information from large-scale multimodal collections has become a critical challenge.
However, existing video retrieval datasets suffer from scope limitations, primarily focusing on matching descriptive but vague queries with small collections of professionally edited, English-centric videos.
To address this gap, we introduce $\textbf{MultiVENT 2.0}$, a large-scale, multilingual event-centric video retrieval benchmark featuring a collection of more than 218,000 news videos and 3,906 queries targeting specific world events.
These queries specifically target information found in the visual content, audio, embedded text, and text metadata of the videos, requiring systems leverage all these sources to succeed at the task.
Preliminary results show that state-of-the-art vision-language models struggle significantly with this task, and while alternative approaches show promise, they are still insufficient to adequately address this problem.
These findings underscore the need for more robust multimodal retrieval systems, as effective video retrieval is a crucial step towards multimodal content understanding and generation.
Kriz, Reno,Sanders, Kate,Etter, David,Murray, Kenton,Carpenter, Cameron,Van Ochten, Kelly,Recknor, Hannah,Guallar-Blasco, Jimena,Martin, Alexander,Colaianni, Ronald,King, Nolan,Yang, Eugene,Van Durme, Benjamin, 2024, MultiVENT 2.0: A Massive Multilingual Benchmark for Event-Centric Video Retrieval
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