  Standard approaches for video recognition usually operate on the full input
videos, which is inefficient due to the widely present spatio-temporal
redundancy in videos. Recent progress in masked video modelling, i.e.,
VideoMAE, has shown the ability of vanilla Vision Transformers (ViT) to
complement spatio-temporal contexts given only limited visual contents.
Inspired by this, we propose propose Masked Action Recognition (MAR), which
reduces the redundant computation by discarding a proportion of patches and
operating only on a part of the videos. MAR contains the following two
indispensable components: cell running masking and bridging classifier.
Specifically, to enable the ViT to perceive the details beyond the visible
patches easily, cell running masking is presented to preserve the
spatio-temporal correlations in videos, which ensures the patches at the same
spatial location can be observed in turn for easy reconstructions.
Additionally, we notice that, although the partially observed features can
reconstruct semantically explicit invisible patches, they fail to achieve
accurate classification. To address this, a bridging classifier is proposed to
bridge the semantic gap between the ViT encoded features for reconstruction and
the features specialized for classification. Our proposed MAR reduces the
computational cost of ViT by 53% and extensive experiments show that MAR
consistently outperforms existing ViT models with a notable margin. Especially,
we found a ViT-Large trained by MAR outperforms the ViT-Huge trained by a
standard training scheme by convincing margins on both Kinetics-400 and
Something-Something v2 datasets, while our computation overhead of ViT-Large is
only 14.5% of ViT-Huge.
