  Leading methods in the domain of action recognition try to distill
information from both the spatial and temporal dimensions of an input video.
Methods that reach State of the Art (SotA) accuracy, usually make use of 3D
convolution layers as a way to abstract the temporal information from video
frames. The use of such convolutions requires sampling short clips from the
input video, where each clip is a collection of closely sampled frames. Since
each short clip covers a small fraction of an input video, multiple clips are
sampled at inference in order to cover the whole temporal length of the video.
This leads to increased computational load and is impractical for real-world
applications. We address the computational bottleneck by significantly reducing
the number of frames required for inference. Our approach relies on a temporal
transformer that applies global attention over video frames, and thus better
exploits the salient information in each frame. Therefore our approach is very
input efficient, and can achieve SotA results (on Kinetics dataset) with a
fraction of the data (frames per video), computation and latency. Specifically
on Kinetics-400, we reach $80.5$ top-1 accuracy with $\times 30$ less frames
per video, and $\times 40$ faster inference than the current leading method.
Code is available at: https://github.com/Alibaba-MIIL/STAM
