LAION-BVD: A 10-Million-Hour Open Video Dataset for Multimodal Pre-training
LAION-BVD is a 10M-hour open video dataset with 1.3B URLs, supporting large-scale multimodal pretraining with content-aware scene detection.
Key Findings
Methodology
This work constructs LAION-BVD by filtering 1.3 billion video URLs from CommonCrawl using content-aware scene detection, downloading 80 million videos totaling 10 million hours. Scene change detection segments videos into clips, which are then captioned automatically using models like Qwen-VL-2B-Instruct for video, Audio Flamingo for audio, and DeepSeek-VL for keyframes. The pipeline ensures high-quality, diverse multimodal data suitable for pretraining. Distributed infrastructure and filtering strategies enable scalable data collection, while automated captioning provides rich annotations for training models such as ViCLIP, CLAP, and CLIP. The dataset supports multiple downstream tasks, validated through extensive experiments.
Key Results
- Models trained on LAION-BVD outperform previous datasets like InternVid on video-text retrieval benchmarks, with ViCLIP achieving 42.7% Top-1 accuracy on MSR-VTT, surpassing 38.7%. In audio-text retrieval, models trained on 55M clips show strong performance, with cross-modal retrieval metrics indicating high semantic alignment. In image-text tasks, models trained on 300M frames achieved a FID of 33.92, demonstrating the dataset's diversity and quality. Performance scales positively with data size and model complexity, confirming the dataset's effectiveness.
- Performance improvements are consistent across tasks: increasing from 10M to 50M samples yields 1-2% gains in accuracy, validating the scaling hypothesis. The dataset's multimodal content, including long videos (up to 30 minutes), supports complex understanding tasks. Experiments also show that content-aware scene detection and automatic captioning significantly enhance data quality, leading to better downstream results.
- The experimental results confirm that LAION-BVD provides a valuable resource for training large multimodal models, enabling better generalization and semantic understanding across video, audio, and image modalities. The dataset's diversity and scale address previous limitations in open video datasets, fostering advancements in video understanding, retrieval, and multimodal generation.
Significance
LAION-BVD represents a milestone in open multimodal datasets, offering an unprecedented scale of web videos for research and industry. Its comprehensive coverage, diverse topics, and multilingual content enable training of more robust, generalizable models. The dataset addresses critical bottlenecks in data scarcity and annotation costs, democratizing access to high-quality multimodal data. This resource accelerates progress in video understanding, cross-modal retrieval, and multimodal AI applications, bridging gaps between academia and industry, and fostering innovation in AI systems capable of understanding complex real-world scenarios.
Technical Contribution
This work introduces a scalable, content-aware pipeline for large-scale web video collection, leveraging scene change detection, automated captioning, and distributed data processing. It combines multiple state-of-the-art models for multimodal annotation, ensuring high-quality, diverse data. The dataset's construction methodology enables efficient scaling from billions of URLs to hundreds of millions of annotated clips and frames. The experimental validation demonstrates that models trained on LAION-BVD outperform existing datasets, establishing new benchmarks for video-text and audio-text retrieval. The approach also offers a blueprint for future large-scale multimodal data curation.
Novelty
LAION-BVD is the first to systematically integrate content-aware scene detection with automated multimodal captioning at such a scale, from billions of web video URLs. Its pipeline ensures high-quality, diverse, and multimodal annotations, addressing previous limitations of scale, diversity, and annotation noise. The dataset's scale and automatic annotation process represent a significant step forward compared to prior efforts like MSR-VTT or WebVid, which relied heavily on manual labeling or smaller corpora. This innovation paves the way for future large-scale, open multimodal datasets.
Limitations
- 依赖Web平台，可能包含不良内容或偏差，影响模型的泛化能力和公平性。
- 自动字幕存在噪声和偏差，可能引入误导信息，影响训练效果。
- 大规模数据采集和处理成本高，资源消耗巨大，限制了实时更新和多样性扩展。
Future Work
未来将优化字幕生成算法，提升描述准确性，减少噪声。同时，探索多语种、多文化内容的多模态融合技术，增强模型的跨语言能力。还计划引入主动学习和人类反馈机制，提升数据标注质量。未来还将结合强化学习，优化模型在复杂场景下的理解能力，推动多模态预训练向更高水平发展。
AI Executive Summary
LAION-BVD stands as the largest open web video dataset to date, comprising 1.3 billion URLs and 80 million videos, totaling 10 million hours of content. This monumental resource was created through an innovative pipeline that employs content-aware scene detection to segment videos into meaningful clips, which are then automatically captioned across video, audio, and static frames using advanced multimodal models like Qwen-VL-2B-Instruct and Audio Flamingo. This approach ensures high-quality, diverse, and richly annotated data suitable for large-scale pretraining.
The significance of LAION-BVD lies in its scale and diversity, addressing the longstanding bottleneck of data scarcity in open video datasets. Its construction enables training models such as ViCLIP, CLAP, and CLIP at unprecedented scales, resulting in substantial performance gains across tasks like video-text retrieval, action recognition, and image-text matching. Experimental results demonstrate that models trained on LAION-BVD outperform prior datasets, with performance scaling positively with data size and model complexity. For example, ViCLIP trained on 50M samples achieves 42.7% accuracy on MSR-VTT, surpassing previous benchmarks.
This dataset opens new avenues for research and industry, facilitating the development of more robust, generalizable multimodal AI systems. Its content-rich, multilingual, and long-duration videos support complex understanding tasks, from long-horizon reasoning to cross-modal retrieval. Despite its success, challenges remain, including content bias, noise in automatic captions, and high computational costs. Future efforts will focus on refining annotation quality, expanding multilingual coverage, and enabling real-time data updates. Overall, LAION-BVD sets a new standard for open multimodal video datasets, promising to accelerate AI progress in understanding and generating complex multimedia content.
Deep Analysis
Background
多模态学习在人工智能领域快速崛起，视频、音频和图像作为丰富的感知信息源，推动了跨模态理解、检索和生成任务的突破。早期数据集如MSR-VTT、YouCook2等规模有限，难以支撑深度模型训练。近年来，LAION-5B等大规模图像文本数据集极大推动了视觉语言模型的发展，但视频资源仍受限于数据获取难题。现有最大视频数据集如InternVid仅有7M视频，远不能满足深度学习需求。Web内容的爆炸式增长带来了丰富的潜在数据，但如何高效筛选、标注和利用这些视频成为研究热点。内容感知场景检测和自动字幕生成技术的出现，为大规模多模态数据的构建提供了新思路。
Core Problem
核心问题在于如何从海量Web视频中高效筛选出高质量、多样化的多模态训练样本。现有数据集规模有限，且标注成本高、质量难控，限制了模型的泛化能力。Web视频内容偏差大，缺乏统一的内容感知和多模态标注机制，导致模型在复杂场景下表现不足。如何利用自动化技术实现大规模筛选、场景检测和字幕生成，成为亟待解决的关键难题。这不仅关系到数据的规模，更影响模型的多样性和鲁棒性。
Innovation
本研究的创新点主要包括：1）引入内容感知场景检测技术，自动识别视频中的关键场景，筛选出代表性剪辑，提升数据质量；2）结合多模态预训练模型（如Qwen-VL-2B-Instruct、Audio Flamingo）自动生成视频、音频和图像字幕，极大提高标注效率和准确性；3）采用分布式大规模数据采集流程，从13亿Web视频URL中筛选出1.3亿候选，最终下载80M视频，累计10M小时，规模远超以往公开数据集。这一流程实现了从海量Web内容到高质量多模态训练样本的快速转化，为未来大规模预训练提供了技术基础。
Methodology
- �� 利用CommonCrawl元数据筛选平台支持的视频链接（如YouTube、Vimeo、Dailymotion）；
- �� 使用cc2dataset工具结合Apache Spark集群，从13亿视频URL中高效提取候选，筛选出1.3B高质量视频；
- �� 通过分布式架构（2000台虚拟服务器，yt-dlp和代理网络）成功下载80M视频，总时长达10M小时；
- �� 利用PySceneDetect进行场景变化检测，筛选出10秒到30分钟的片段，剔除静态片段；
- �� 采用Qwen-VL-2B-Instruct对视频采样最多32帧，自动描述内容，生成视频字幕；
- �� 使用Audio Flamingo描述音频内容，确保描述长度符合音频文本数据特性；
- �� 通过ffmpeg提取场景变化关键帧，过滤黑帧，生成300M图像，用于图像-文本任务；
- �� 最终形成55M视频剪辑和300M关键帧，作为多模态预训练基础。
Experiments
采用多任务验证：在MSR-VTT、MSVD等数据集上进行视频文本匹配，验证模型在视频理解中的表现；在UCF-101、Kinetics-400上进行动作识别，验证模型的泛化能力；在AudioCaps等音频文本检索任务中评估模型跨模态对齐效果。模型训练采用对比学习（如InfoNCE损失），在不同数据规模（10M、50M）上验证性能提升。与基线如InternVid和DataComp-1B对比，结果显示：模型性能随数据规模增长显著提升，ViCLIP在MSR-VTT上的Top-1准确率由38.7%提升至42.7%，在UCF-101达79.7%。此外，模型在图像文本检索中FID值为33.92，验证数据多样性和标注质量。
Results
模型在多项任务中表现优异，尤其是在视频理解和跨模态检索方面。50M样本的ViCLIP在UCF-101达79.7%的Top-1准确率，比10M样本提升1.1个百分点。模型性能与数据规模呈正相关，验证了大规模多模态数据的有效性。模型在图像-文本检索中表现出色，FID值低于行业平均水平，显示出数据的多样性和标注质量的提升。实验还表明，内容感知场景检测和字幕自动生成显著改善了数据的语义表达，为未来多模态预训练提供了坚实基础。
Applications
该数据集可广泛应用于视频理解、跨模态检索、内容生成和多模态基础模型训练。企业和研究机构可以利用LAION-BVD训练更强大的视频和音频模型，提升内容推荐、智能问答和多模态交互系统的性能。其多语种、多场景的特性，有助于推动多文化、多语言环境下的AI应用，满足实际多样化需求。
Limitations & Outlook
数据依赖Web平台，可能包含不良内容或偏差，影响模型的泛化。自动字幕存在噪声和偏差，可能引入误导信息。大规模数据采集和处理成本高，资源消耗巨大，限制了实时更新和多样性扩展。未来需加强内容过滤和标注质量控制，提升模型鲁棒性。
Plain Language Accessible to non-experts
想象你在一个超级大的工厂，工厂每天接收成千上万的原材料（视频、音频和图片），工人们需要快速筛选出有用的部分（场景变化、关键场景），并用简单的话描述内容（字幕）。这些描述帮助工厂的机器人（模型）学习理解不同的场景和声音。为了让机器人更聪明，工厂还会不断提供新材料，教它识别不同的场景、声音和图片。通过不断练习，机器人变得越来越擅长理解复杂的场景，比如体育比赛、音乐会或新闻报道。这个过程就像我们用大量的图片和视频训练AI，让它能像人一样理解世界。
ELI14 Explained like you're 14
想象你在一个超级大的学校，学校每天都有很多学生带来各种各样的视频、图片和声音。老师们想让学生学会理解这些内容，比如视频里的人在做什么、声音里讲了什么。可是，学生们要学习的内容太多了，老师不能一个个教。于是，老师用一种聪明的机器（叫模型）帮忙，把视频切成小段，给每段写上简单的描述，就像老师给每个场景写一句话。这样，机器就能通过看很多很多视频和听很多声音，学会理解不同的场景和声音。这个研究就像让机器变得像个超级聪明的学生，能看懂各种视频和声音，还能帮我们找到想要的内容，比如找出体育比赛的视频或者音乐会的片段。
Glossary
Content-aware scene detection (内容感知场景检测)
一种自动识别视频中场景变化的技术，帮助筛选出高质量的剪辑。技术上通过分析帧间差异或运动信息实现。
在论文中用于从海量视频中提取具有代表性的场景段落。
Contrastive learning (对比学习)
一种训练模型的方法，通过最大化正样本对的相似度和最小化负样本对的相似度，学习跨模态嵌入空间。
在模型训练中用于优化视频、音频和文本的跨模态对齐。
FID (Fréchet Inception Distance, 弗雷谢特距离)
一种衡量生成图像与真实图像分布差异的指标，数值越低代表生成质量越高。
用于评估模型在图像-文本检索中的表现。
ViCLIP
基于对比学习的多模态视频文本匹配模型，能在视频和文本之间建立语义关联。
作为本研究的核心模型之一，用于验证数据集的有效性。
CLAP
音频-文本对比学习模型，用于音频内容的语义理解和检索。
在音频检索任务中评估模型性能。
Open Questions Unanswered questions from this research
- 1 如何进一步提升字幕自动生成的准确性，减少噪声对模型训练的影响，是未来的重要研究方向。
- 2 多语种、多文化内容的多模态数据如何有效整合，仍需探索更优的多模态融合技术。
- 3 大规模视频数据的实时处理和更新机制尚未成熟，未来需开发更高效的算法和基础设施。
Applications
Immediate Applications
视频内容检索
利用LAION-BVD训练的模型，可以快速检索海量视频中的特定场景或内容，提升内容管理和推荐系统的效率。
多模态内容生成
基于丰富的多模态数据，支持自动生成视频字幕、音频描述和图像标签，增强多媒体交互体验。
Long-term Vision
智能多模态AI助手
未来可以基于LAION-BVD数据训练出更具理解能力的AI助手，实现跨场景、多模态的自然交互，应用于教育、娱乐和工业自动化。
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.