Collaboratively Self-supervised Video Representation Learning for Action Recognition
This work addresses the need for improved self-supervised learning in video action recognition, offering a novel multi-task approach that is incremental in combining existing techniques.
The paper tackles the problem of self-supervised video representation learning for action recognition by proposing a framework that jointly uses generative pose prediction and discriminative context matching as pretext tasks, achieving state-of-the-art performance on multiple popular video datasets.
Considering the close connection between action recognition and human pose estimation, we design a Collaboratively Self-supervised Video Representation (CSVR) learning framework specific to action recognition by jointly factoring in generative pose prediction and discriminative context matching as pretext tasks. Specifically, our CSVR consists of three branches: a generative pose prediction branch, a discriminative context matching branch, and a video generating branch. Among them, the first one encodes dynamic motion feature by utilizing Conditional-GAN to predict the human poses of future frames, and the second branch extracts static context features by contrasting positive and negative video feature and I-frame feature pairs. The third branch is designed to generate both current and future video frames, for the purpose of collaboratively improving dynamic motion features and static context features. Extensive experiments demonstrate that our method achieves state-of-the-art performance on multiple popular video datasets.