  This paper addresses the problem of self-supervised video representation
learning from a new perspective -- by video pace prediction. It stems from the
observation that human visual system is sensitive to video pace, e.g., slow
motion, a widely used technique in film making. Specifically, given a video
played in natural pace, we randomly sample training clips in different paces
and ask a neural network to identify the pace for each video clip. The
assumption here is that the network can only succeed in such a pace reasoning
task when it understands the underlying video content and learns representative
spatio-temporal features. In addition, we further introduce contrastive
learning to push the model towards discriminating different paces by maximizing
the agreement on similar video content. To validate the effectiveness of the
proposed method, we conduct extensive experiments on action recognition and
video retrieval tasks with several alternative network architectures.
Experimental evaluations show that our approach achieves state-of-the-art
performance for self-supervised video representation learning across different
network architectures and different benchmarks. The code and pre-trained models
are available at https://github.com/laura-wang/video-pace.
