  In self-supervised spatio-temporal representation learning, the temporal
resolution and long-short term characteristics are not yet fully explored,
which limits representation capabilities of learned models. In this paper, we
propose a novel self-supervised method, referred to as video Playback Rate
Perception (PRP), to learn spatio-temporal representation in a
simple-yet-effective way. PRP roots in a dilated sampling strategy, which
produces self-supervision signals about video playback rates for representation
model learning. PRP is implemented with a feature encoder, a classification
module, and a reconstructing decoder, to achieve spatio-temporal semantic
retention in a collaborative discrimination-generation manner. The
discriminative perception model follows a feature encoder to prefer perceiving
low temporal resolution and long-term representation by classifying
fast-forward rates. The generative perception model acts as a feature decoder
to focus on comprehending high temporal resolution and short-term
representation by introducing a motion-attention mechanism. PRP is applied on
typical video target tasks including action recognition and video retrieval.
Experiments show that PRP outperforms state-of-the-art self-supervised models
with significant margins. Code is available at github.com/yuanyao366/PRP
