Video Representation Learning with Visual Tempo Consistency
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Visual tempo, which describes how fast an action goes, has shown its potential in supervised action recognition. In this work, we demonstrate that visual tempo can also serve as a self-supervision signal for video representation learning. We propose to maximize the mutual information between representations of slow and fast videos via hierarchical contrastive learning (VTHCL). Specifically, by sampling the same instance at slow and fast frame rates respectively, we can obtain slow and fast video frames which share the same semantics but contain different visual tempos. Video representations learned from VTHCL achieve the competitive performances under the self-supervision evaluation protocol for action recognition on UCF-101 (82.1%) and HMDB-51 (49.2%). Moreover, we show that the learned representations are also generalized well to other downstream tasks including action detection on AVA and action anticipation on Epic-Kitchen. Finally, our empirical analysis suggests that a more thorough evaluation protocol is needed to verify the effectiveness of the self-supervised video representations across network structures and downstream tasks.
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