  We introduce InternVideo2, a new family of video foundation models (ViFM)
that achieve the state-of-the-art results in video recognition, video-text
tasks, and video-centric dialogue. Our core design is a progressive training
approach that unifies the masked video modeling, crossmodal contrastive
learning, and next token prediction, scaling up the video encoder size to 6B
parameters. At the data level, we prioritize spatiotemporal consistency by
semantically segmenting videos and generating video-audio-speech captions. This
improves the alignment between video and text. Through extensive experiments,
we validate our designs and demonstrate superior performance on over 60 video
and audio tasks. Notably, our model outperforms others on various video-related
dialogue and long video understanding benchmarks, highlighting its ability to
reason and comprehend longer contexts. Code and models are available at
https://github.com/OpenGVLab/InternVideo/tree/main/InternVideo2/.
