MultiVENT-Raw: A Benchmark for Retrieval and Reasoning over Raw Videos

2026-09-23

AI Summary

为解决原始视频信息检索和理解难题，发布包含近12万条视频的多语言数据集MultiVENT-Raw，并支持检索和生成任务。

Abstract

Online information is increasingly consumed in video format. Much of this comes in the form of *raw video*: continuous footage taken on a cell phone, with a hand-held camera, or via CCTV, which is then directly uploaded to social media platforms and content sharing services. Whereas professional or even amateur-edited footage tends to feature scripted speech, chyrons, graphics, and metadata that help contextualize its subject matter, raw video typically contains none of these things, making it a much more challenging medium for information retrieval and machine understanding. To facilitate progress in this domain, we release MultiVENT-Raw, a multilingual collection of nearly 120,000 primarily raw videos (over 5,300 total hours), paired with 130 events and 222 event-centric queries, along with human-annotated video relevance judgments and human-extracted key facts for relevant videos. MultiVENT-Raw supports both a retrieval task---to identify videos in the collection relevant to a query event---and a generation task---to summarize event-related videos into a coherent report for a target user. We benchmark strong baselines on MultiVENT-Raw, showing both tasks to be challenging even for some of the latest multimodal models.

Problem

• raw video
• information retrieval
• machine understanding

Innovation

• raw video
• multimodal models
• information retrieval
• machine understanding
• benchmark