MultiVENT 2.0: A Massive Multilingual Benchmark for Event-Centric Video
  Retrieval
Authors: name: Reno Kriz, name: Kate Sanders, name: David Etter, name: Kenton Murray, name: Cameron Carpenter, name: Kelly Van Ochten, name: Hannah Recknor, name: Jimena Guallar-Blasco, name: Alexander Martin, name: Ronald Colaianni, name: Nolan King, name: Eugene Yang, name: Benjamin Van Durme

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.