  Visual-only self-supervised learning has achieved significant improvement in
video representation learning. Existing related methods encourage models to
learn video representations by utilizing contrastive learning or designing
specific pretext tasks. However, some models are likely to focus on the
background, which is unimportant for learning video representations. To
alleviate this problem, we propose a new view called long-range residual frame
to obtain more motion-specific information. Based on this, we propose the
Motion-Contrastive Perception Network (MCPNet), which consists of two branches,
namely, Motion Information Perception (MIP) and Contrastive Instance Perception
(CIP), to learn generic video representations by focusing on the changing areas
in videos. Specifically, the MIP branch aims to learn fine-grained motion
features, and the CIP branch performs contrastive learning to learn overall
semantics information for each instance. Experiments on two benchmark datasets
UCF-101 and HMDB-51 show that our method outperforms current state-of-the-art
visual-only self-supervised approaches.
