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The construction of models for video action classification progressesrapidly. However, the performance of those models can still be easily improvedby ensembling with the same models trained on different modalities (e.g.Optical flow). Unfortunately, it is computationally expensive to use severalmodalities during inference. Recent works examine the ways to integrateadvantages of multi-modality into a single RGB-model. Yet, there is still aroom for improvement. In this paper, we explore the various methods to embedthe ensemble power into a single model. We show that proper initialization, aswell as mutual modality learning, enhances single-modality models. As a result,we achieve state-of-the-art results in the Something-Something-v2 benchmark.

Mutual Modality Learning for Video Action Classification