Video Cloze Procedure for Self-Supervised Spatio-Temporal Learning


  • Dezhao Luo Chinese Academy of Sciences
  • Chang Liu University of Chinese Academy of Sciences
  • Yu Zhou Chinese Academy of Sciences
  • Dongbao Yang Chinese Academy of Sciences
  • Can Ma Chinese Academy of Sciences
  • Qixiang Ye University of Chinese Academy of Sciences
  • Weiping Wang Chinese Academy of Sciences



We propose a novel self-supervised method, referred to as Video Cloze Procedure (VCP), to learn rich spatial-temporal representations. VCP first generates “blanks” by withholding video clips and then creates “options” by applying spatio-temporal operations on the withheld clips. Finally, it fills the blanks with “options” and learns representations by predicting the categories of operations applied on the clips. VCP can act as either a proxy task or a target task in self-supervised learning. As a proxy task, it converts rich self-supervised representations into video clip operations (options), which enhances the flexibility and reduces the complexity of representation learning. As a target task, it can assess learned representation models in a uniform and interpretable manner. With VCP, we train spatial-temporal representation models (3D-CNNs) and apply such models on action recognition and video retrieval tasks. Experiments on commonly used benchmarks show that the trained models outperform the state-of-the-art self-supervised models with significant margins.




How to Cite

Luo, D., Liu, C., Zhou, Y., Yang, D., Ma, C., Ye, Q., & Wang, W. (2020). Video Cloze Procedure for Self-Supervised Spatio-Temporal Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 11701-11708.



AAAI Technical Track: Vision