  Contrastive learning applied to self-supervised representation learning has
seen a resurgence in deep models. In this paper, we find that existing
contrastive learning based solutions for self-supervised video recognition
focus on inter-variance encoding but ignore the intra-variance existing in
clips within the same video. We thus propose to learn dual representations for
each clip which (\romannumeral 1) encode intra-variance through a shuffle-rank
pretext task; (\romannumeral 2) encode inter-variance through a temporal
coherent contrastive loss. Experiment results show that our method plays an
essential role in balancing inter and intra variances and brings consistent
performance gains on multiple backbones and contrastive learning frameworks.
Integrated with SimCLR and pretrained on Kinetics-400, our method achieves
$\textbf{82.0\%}$ and $\textbf{51.2\%}$ downstream classification accuracy on
UCF101 and HMDB51 test sets respectively and $\textbf{46.1\%}$ video retrieval
accuracy on UCF101, outperforming both pretext-task based and contrastive
learning based counterparts. Our code is available at
\href{https://github.com/lzhangbj/DualVar}{https://github.com/lzhangbj/DualVar}.
