  Data augmentation is a ubiquitous technique for improving image
classification when labeled data is scarce. Constraining the model predictions
to be invariant to diverse data augmentations effectively injects the desired
representational invariances to the model (e.g., invariance to photometric
variations), leading to improved accuracy. Compared to image data, the
appearance variations in videos are far more complex due to the additional
temporal dimension. Yet, data augmentation methods for videos remain
under-explored. In this paper, we investigate various data augmentation
strategies that capture different video invariances, including photometric,
geometric, temporal, and actor/scene augmentations. When integrated with
existing consistency-based semi-supervised learning frameworks, we show that
our data augmentation strategy leads to promising performance on the
Kinetics-100, UCF-101, and HMDB-51 datasets in the low-label regime. We also
validate our data augmentation strategy in the fully supervised setting and
demonstrate improved performance.
