InternVideo2: Scaling Video Foundation Models for Multimodal Video
  Understanding
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/.
6600d5e3569b30694e4d1080
Yi Wang
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Kunchang Li
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2024-03-25T10:05:21.549Z
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Kunchang Li
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Andy1621
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Xinhao Li
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2024-08-05T10:27:44.058Z
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Xinhao Li
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lixinhao
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Jiashuo Yu
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2024-03-25T10:05:49.550Z
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Jiashuo Yu
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awojustin
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Yinan He
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2024-03-25T10:05:56.422Z
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Yinan He
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Guo Chen
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2024-03-25T11:50:57.140Z
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Guo Chen
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cg1177
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Baoqi Pei
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Rongkun Zheng
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Jilan Xu
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2024-03-25T10:07:10.497Z
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Jilan Xu
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Jazzcharles
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Zun Wang
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2024-08-05T10:28:30.764Z
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Zun Wang
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ZunWang
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Yansong Shi
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2024-08-05T10:28:36.354Z
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Yansong Shi
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systemshi
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Tianxiang Jiang
admin_assigned
2024-03-25T10:07:32.223Z
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Tianxiang Jiang
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txjiang
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Songze Li
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2025-07-03T07:47:46.457Z
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Songze Li
user
SongzeLi
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Hongjie Zhang
claimed_verified
2025-09-25T08:04:06.655Z
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Yansong Shi
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nanamma
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Yifei Huang
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Yu Qiao
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Yali Wang
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Limin Wang
admin_assigned
2024-03-25T10:09:26.539Z
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Limin Wang
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lmwang
InternVideo2: Scaling Video Foundation Models for Multimodal Video
  Understanding
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/.
InternVideo2, a state-of-the-art video foundation model, employs a progressive training approach using multiple self- or weakly-supervised learning paradigms to excel in various video and audio tasks.
video foundation model progressive training paradigm masked video token reconstruction cross-modal contrastive learning next token prediction spatiotemporal consistency semantic segmentation video-audio-speech captions long video understanding