  Despite the great progress in video understanding made by deep convolutional
neural networks, feature representation learned by existing methods may be
biased to static visual cues. To address this issue, we propose a novel method
to suppress static visual cues (SSVC) based on probabilistic analysis for
self-supervised video representation learning. In our method, video frames are
first encoded to obtain latent variables under standard normal distribution via
normalizing flows. By modelling static factors in a video as a random variable,
the conditional distribution of each latent variable becomes shifted and scaled
normal. Then, the less-varying latent variables along time are selected as
static cues and suppressed to generate motion-preserved videos. Finally,
positive pairs are constructed by motion-preserved videos for contrastive
learning to alleviate the problem of representation bias to static cues. The
less-biased video representation can be better generalized to various
downstream tasks. Extensive experiments on publicly available benchmarks
demonstrate that the proposed method outperforms the state of the art when only
single RGB modality is used for pre-training.
