  We propose a self-supervised learning approach for videos that learns
representations of both the RGB frames and the accompanying audio without human
supervision. In contrast to images that capture the static scene appearance,
videos also contain sound and temporal scene dynamics. To leverage the temporal
and aural dimension inherent to videos, our method extends temporal
self-supervision to the audio-visual setting and integrates it with multi-modal
contrastive objectives. As temporal self-supervision, we pose playback speed
and direction recognition in both modalities and propose intra- and inter-modal
temporal ordering tasks. Furthermore, we design a novel contrastive objective
in which the usual pairs are supplemented with additional sample-dependent
positives and negatives sampled from the evolving feature space. In our model,
we apply such losses among video clips and between videos and their temporally
corresponding audio clips. We verify our model design in extensive ablation
experiments and evaluate the video and audio representations in transfer
experiments to action recognition and retrieval on UCF101 and HMBD51, audio
classification on ESC50, and robust video fingerprinting on VGG-Sound, with
state-of-the-art results.
