InternVideo2: Scaling Foundation Models for Multimodal Video Understanding

Document detail

ID

oai:arXiv.org:2403.15377

Topic

Computer Science - Computer Vision...

Author

Wang, Yi Li, Kunchang Li, Xinhao Yu, Jiashuo He, Yinan Wang, Chenting Chen, Guo Pei, Baoqi Yan, Ziang Zheng, Rongkun Xu, Jilan Wang, Zun Shi, Yansong Jiang, Tianxiang Li, Songze Zhang, Hongjie Huang, Yifei Qiao, Yu Wang, Yali Wang, Limin

Year

2024

listing date

8/21/2024

Keywords

models understanding video

Metrics

Abstract

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/.

;Comment: a technical report about video understanding (accepted to ECCV2024)

Wang, Yi,Li, Kunchang,Li, Xinhao,Yu, Jiashuo,He, Yinan,Wang, Chenting,Chen, Guo,Pei, Baoqi,Yan, Ziang,Zheng, Rongkun,Xu, Jilan,Wang, Zun,Shi, Yansong,Jiang, Tianxiang,Li, Songze,Zhang, Hongjie,Huang, Yifei,Qiao, Yu,Wang, Yali,Wang, Limin, 2024, InternVideo2: Scaling Foundation Models for Multimodal Video Understanding