CLIP-ViP: Adapting Pre-trained Image-Text Model to Video-Language Representation Alignment

@article{Xue2022CLIPViPAP,
  title={CLIP-ViP: Adapting Pre-trained Image-Text Model to Video-Language Representation Alignment},
  author={Hongwei Xue and Yuchong Sun and Bei Liu and Jianlong Fu and Rui Song and Houqiang Li and Jiebo Luo},
  journal={ArXiv},
  year={2022},
  volume={abs/2209.06430}
}
The pre-trained image-text models, like CLIP, have demonstrated the strong power of vision-language representation learned from a large scale of web-collected image-text data. In light of the well-learned visual features, some existing works transfer image representation to video domain and achieve good results. However, how to utilize image-language pre-trained model (e.g., CLIP) for video-language pre-training (post-pretraining) is still under ex-plored. In this paper, we investigate two… 

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