We introduce InternVideo2, a new video foundation model (ViFM) that achieves the state-of-the-art performance in action recognition, video-text tasks, and video-centric dialogue. Our approach employs a progressive training paradigm that unifies the different self- or weakly-supervised learning frameworks of masked video token reconstruction, cross-modal contrastive learning, and next token prediction. Different training stages would guide our model to capture different levels of structure and semantic information through different pretext tasks. At the data level, we prioritize the spatiotemporal consistency by semantically segmenting videos and generating video-audio-speech captions. This improves the alignment between video and text. We scale both data and model size for our InternVideo2. Through extensive experiments, we validate our designs and demonstrate the state-of-the-art performance on over 60 video and audio tasks. Notably, our model outperforms others on various video-related captioning, dialogue, and long video understanding benchmarks, highlighting its ability to reason and comprehend long temporal contexts. Code and models are available at https://github.com/OpenGVLab/InternVideo2/.

翻译：我们提出InternVideo2，一种新的视频基础模型（ViFM），在动作识别、视频-文本任务以及以视频为中心的对话中达到了最先进性能。我们的方法采用渐进式训练范式，统一了掩码视频标记重建、跨模态对比学习和下一标记预测等不同自监督或弱监督学习框架。不同训练阶段通过不同的前置任务引导模型捕获不同层次的结构和语义信息。在数据层面，我们通过语义分割视频并生成视频-音频-语音描述，优先考虑时空一致性。这改善了视频与文本之间的对齐。我们扩展了InternVideo2的数据和模型规模。通过大量实验，我们验证了设计，并在超过60项视频和音频任务中展示了最先进性能。值得注意的是，我们的模型在各种与视频相关的描述、对话和长视频理解基准测试中优于其他模型，突显了其推理和理解长时间上下文的能力。代码和模型可在https://github.com/OpenGVLab/InternVideo2/获取。