  State-of-the-art video action classifiers often suffer from overfitting. They
tend to be biased towards specific objects and scene cues, rather than the
foreground action content, leading to sub-optimal generalization performances.
Recent data augmentation strategies have been reported to address the
overfitting problems in static image classifiers. Despite the effectiveness on
the static image classifiers, data augmentation has rarely been studied for
videos. For the first time in the field, we systematically analyze the efficacy
of various data augmentation strategies on the video classification task. We
then propose a powerful augmentation strategy VideoMix. VideoMix creates a new
training video by inserting a video cuboid into another video. The ground truth
labels are mixed proportionally to the number of voxels from each video. We
show that VideoMix lets a model learn beyond the object and scene biases and
extract more robust cues for action recognition. VideoMix consistently
outperforms other augmentation baselines on Kinetics and the challenging
Something-Something-V2 benchmarks. It also improves the weakly-supervised
action localization performance on THUMOS'14. VideoMix pretrained models
exhibit improved accuracies on the video detection task (AVA).
