This paper introduces InternVid, a large-scale video-centric multimodal dataset that enables learning powerful and transferable video-text representations for multimodal understanding and generation. The InternVid dataset contains over 7 million videos lasting nearly 760K hours, yielding 234M video clips accompanied by detailed descriptions of total 4.1B words. Our core contribution is to develop a scalable approach to autonomously build a high-quality video-text dataset with large language models (LLM), thereby showcasing its efficacy in learning video-language representation at scale. Specifically, we utilize a multi-scale approach to generate video-related descriptions. Furthermore, we introduce ViCLIP, a video-text representation learning model based on ViT-L. Learned on InternVid via contrastive learning, this model demonstrates leading zero-shot action recognition and competitive video retrieval performance. Beyond basic video understanding tasks like recognition and retrieval, our dataset and model have broad applications. They are particularly beneficial for generating interleaved video-text data for learning a video-centric dialogue system, advancing video-to-text and text-to-video generation research. These proposed resources provide a tool for researchers and practitioners interested in multimodal video understanding and generation.

翻译：本文提出InternVid，一个大规模以视频为中心的多模态数据集，能够学习强大且可迁移的视频-文本表征，用于多模态理解与生成。InternVid数据集包含超过700万段视频，总时长近76万小时，生成了2.34亿个视频片段，并附有总计41亿词的详细描述。我们的核心贡献是开发了一种可扩展的方法，利用大语言模型自动构建高质量的视频-文本数据集，从而展示了该方法在大规模学习视频-语言表征方面的有效性。具体而言，我们采用多尺度方法生成与视频相关的描述。此外，我们提出了ViCLIP，一种基于ViT-L的视频-文本表征学习模型。通过对比学习在InternVid上进行训练，该模型在零样本动作识别和具有竞争力的视频检索任务中表现出领先性能。除了识别和检索等基础视频理解任务外，我们的数据集和模型具有广泛的应用前景。它们特别有助于生成交织的视频-文本数据，以学习以视频为中心的对话系统，并推动视频到文本及文本到视频生成研究。这些提出的资源为关注多模态视频理解与生成的研究人员和实践者提供了有力工具。