  Contrastive learning of auditory and visual perception has been extremely
successful when investigated individually. However, there are still major
questions on how we could integrate principles learned from both domains to
attain effective audiovisual representations. In this paper, we present a
contrastive framework to learn audiovisual representations from unlabeled
videos. The type and strength of augmentations utilized during self-supervised
pre-training play a crucial role for contrastive frameworks to work
sufficiently. Hence, we extensively investigate composition of temporal
augmentations suitable for learning audiovisual representations; we find lossy
spatio-temporal transformations that do not corrupt the temporal coherency of
videos are the most effective. Furthermore, we show that the effectiveness of
these transformations scales with higher temporal resolution and stronger
transformation intensity. Compared to self-supervised models pre-trained on
only sampling-based temporal augmentation, self-supervised models pre-trained
with our temporal augmentations lead to approximately 6.5% gain on linear
classifier performance on AVE dataset. Lastly, we show that despite their
simplicity, our proposed transformations work well across self-supervised
learning frameworks (SimSiam, MoCoV3, etc), and benchmark audiovisual dataset
(AVE).
