  This paper studies how to introduce viewpoint-invariant feature
representations that can help action recognition and detection. Although we
have witnessed great progress of action recognition in the past decade, it
remains challenging yet interesting how to efficiently model the geometric
variations in large scale datasets. This paper proposes a novel
Spatial-Temporal Alignment Network (STAN) that aims to learn geometric
invariant representations for action recognition and action detection. The STAN
model is very light-weighted and generic, which could be plugged into existing
action recognition models like ResNet3D and the SlowFast with a very low extra
computational cost. We test our STAN model extensively on AVA, Kinetics-400,
AVA-Kinetics, Charades, and Charades-Ego datasets. The experimental results
show that the STAN model can consistently improve the state of the arts in both
action detection and action recognition tasks. We will release our data, models
and code.
