Multivent 2.0: A Massive Multilingual Benchmark For Event-centric 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 fin

Stats

• citations2
• S2 citations—
• github stars0
• HF likes0
• heat score3.58
• arxiv keykriz2024multivent

Related papers

• MUVR: A Multi-modal Untrimmed Video Retrieval Benchmark With Multi-level Visual Correspondence (2025)1.40
• Verve: Versatile Retrieval For Videos Via Unified Embeddings (2026)0.00
• MMMORRF: Multimodal Multilingual Modularized Reciprocal Rank Fusion (2025)2.26
• Multi-query Video Retrieval (2022)9.59
• Enhanced Multimodal Video Retrieval System: Integrating Query Expansion And Cross-modal Temporal Event Retrieval (2025)0.00
• CBVS: A Large-scale Chinese Image-text Benchmark For Real-world Short Video Search Scenarios (2024)2.29
• Multimodal Lengthy Videos Retrieval Framework And Evaluation Metric (2025)0.00
• Mumur : Multilingual Multimodal Universal Retrieval (2022)2.26