  Deep-Learning-based video recognition has shown promising improvements along
with the development of large-scale datasets and spatiotemporal network
architectures. In image recognition, learning spatially invariant features is a
key factor in improving recognition performance and robustness. Data
augmentation based on visual inductive priors, such as cropping, flipping,
rotating, or photometric jittering, is a representative approach to achieve
these features. Recent state-of-the-art recognition solutions have relied on
modern data augmentation strategies that exploit a mixture of augmentation
operations. In this study, we extend these strategies to the temporal dimension
for videos to learn temporally invariant or temporally localizable features to
cover temporal perturbations or complex actions in videos. Based on our novel
temporal data augmentation algorithms, video recognition performances are
improved using only a limited amount of training data compared to the
spatial-only data augmentation algorithms, including the 1st Visual Inductive
Priors (VIPriors) for data-efficient action recognition challenge. Furthermore,
learned features are temporally localizable that cannot be achieved using
spatial augmentation algorithms. Our source code is available at
https://github.com/taeoh-kim/temporal_data_augmentation.
