LAION-BVD: A 10-Million-Hour Open Video Dataset for Multimodal Pre-Training

MCML Authors

Almut Sophia Koepke

Dr.

JRG Leader Multi-Modal Learning

Computer Vision & Artificial Intelligence

Abstract

We present LAION-BVD, a large-scale open video dataset for multimodal learning, which contains 1.3B platform-specific video URLs collected from CommonCrawl. From these, we download 80M videos with a total duration of 10 million hours. The dataset is designed for multimodal pre-training across the video, audio, and image modalities. Using content-aware scene detection, we extract clips for which we synthetically generate video and audio captions. Models trained on these data achieve competitive performance on standard video-text and audio-text benchmarks, with consistent improvements as training or model scale increases. Additionally, we explore video frames as an alternative source of image-text data by extracting scene-changing frames. These frames exhibit a visual distribution distinct from standard web image corpora, and models trained on this dataset achieve strong image-text retrieval performance. We release LAION-BVD to the research community. It significantly expands open access to multimodal videos at an unprecedented scale.

misc HNC+26

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Preprint

Aug. 2026

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Authors

A. Hochlehnert • M. Nezhurina • M. Cherti • A. Radonjic • T. Wiedemer • C. Schuhmann • R. Beaumont • W. Brendel • B. Schölkopf • A. S. Koepke • J. Jitsev • M. Bethge

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Links

arXiv URL

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Research Area

 B1 | Computer Vision