MultiVENT 2.0: A Massive Multilingual Benchmark for Event-Centric Video Retrieval

TL;DR

MultiVENT 2.0 introduces a large-scale, multilingual benchmark for event-centric video retrieval, highlighting current models' struggles and emphasizing the need for more robust multimodal retrieval systems.

Contribution

The paper presents MultiVENT 2.0, a comprehensive multilingual dataset with over 218,000 videos and nearly 4,000 queries, addressing limitations of existing datasets and challenging current models.

Findings

State-of-the-art models perform poorly on the benchmark.

Alternative approaches show potential but are still inadequate.

Highlights the importance of multimodal integration for video retrieval.

Abstract

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.