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

arXiv:2608.24845 · cs.CV, cs.AI, cs.LG · Submitted 2026-08-25 · Read on arXiv

cs.CV, cs.AI, cs.LG

Submitted: 2026-08-25

Updated: 2026-08-25

Code: https://github.com/rom1504/cc2dataset

License: http://creativecommons.org/licenses/by/4.0/

The gist: We present LAION-BVD, a large-scale open video dataset for multimodal learning, which contains 1.3B platform-specific video URLs collected from CommonCrawl.

Terminology

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.

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