title: MultiVENT 2.0: A Massive Multilingual Benchmark for Event-Centric Video
    Retrieval
summary: 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.
paper_authors._id: 670fcf2d5a714a5429d5d38c
paper_authors.name: Reno Kriz
paper_authors._id: 670fcf2d5a714a5429d5d38d
paper_authors.name: Kate Sanders
paper_authors._id: 670fcf2d5a714a5429d5d38e
paper_authors.name: David Etter
paper_authors._id: 670fcf2d5a714a5429d5d38f
paper_authors.name: Kenton Murray
paper_authors._id: 670fcf2d5a714a5429d5d390
paper_authors.name: Cameron Carpenter
paper_authors._id: 670fcf2d5a714a5429d5d391
paper_authors.name: Kelly Van Ochten
paper_authors._id: 670fcf2d5a714a5429d5d392
paper_authors.name: Hannah Recknor
paper_authors._id: 670fcf2d5a714a5429d5d393
paper_authors.name: Jimena Guallar-Blasco
paper_authors._id: 670fcf2d5a714a5429d5d394
paper_authors.name: Alexander Martin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-08T07:00:31.566Z
paper_authors._id: 644ffb154f731658826945c8
paper_authors.avatarUrl: /avatars/46fa3a15c2b974692bee5b6ed6468be0.svg
paper_authors.fullname: Alex Martin
paper_authors.type: user
paper_authors.user: alexmartin1722
paper_authors._id: 670fcf2d5a714a5429d5d395
paper_authors.name: Ronald Colaianni
paper_authors._id: 670fcf2d5a714a5429d5d396
paper_authors.name: Nolan King
paper_authors._id: 670fcf2d5a714a5429d5d397
paper_authors.name: Eugene Yang
paper_authors._id: 670fcf2d5a714a5429d5d398
paper_authors.name: Benjamin Van Durme
paper_title: MultiVENT 2.0: A Massive Multilingual Benchmark for Event-Centric Video
    Retrieval
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: vision-language models multimodal retrieval event-centric news videos multilingual