MultiVENT 2.0: A Massive Multilingual Benchmark for Event-Centric Video
  Retrieval
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
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 tasks.
670fcf2d5a714a5429d5d38c
Reno Kriz
670fcf2d5a714a5429d5d38d
Kate Sanders
670fcf2d5a714a5429d5d38e
David Etter
670fcf2d5a714a5429d5d38f
Kenton Murray
670fcf2d5a714a5429d5d390
Cameron Carpenter
670fcf2d5a714a5429d5d391
Kelly Van Ochten
670fcf2d5a714a5429d5d392
Hannah Recknor
670fcf2d5a714a5429d5d393
Jimena Guallar-Blasco
670fcf2d5a714a5429d5d394
Alexander Martin
claimed_verified
2025-04-08T07:00:31.566Z
644ffb154f731658826945c8
/avatars/46fa3a15c2b974692bee5b6ed6468be0.svg
Alex Martin
user
alexmartin1722
670fcf2d5a714a5429d5d395
Ronald Colaianni
670fcf2d5a714a5429d5d396
Nolan King
670fcf2d5a714a5429d5d397
Eugene Yang
670fcf2d5a714a5429d5d398
Benjamin Van Durme
MultiVENT 2.0: A Massive Multilingual Benchmark for Event-Centric Video
  Retrieval
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
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 tasks.
MultiVENT 2.0 introduces a large-scale, multilingual event-centric video retrieval benchmark, highlighting the inadequacy of current vision-language models in handling multimodal data.
vision-language models multimodal retrieval event-centric news videos multilingual