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

2026-08-25

AI Summary

为解决多模态预训练数据需求，通过收集和处理1.3亿个视频URL生成LAION-BVD数据集，采用内容感知场景检测和合成字幕技术，支持视频、音频及图像模态学习。

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.

Problem

• multimodal pre-training
• large-scale dataset
• video dataset
• audio-text
• image-text

Innovation

• Multimodal Pre-training
• Content-aware Scene Detection
• Synthetic Caption Generation