Spatial-Temporal Alignment Network for Action Recognition
Jinhui Ye Junwei Liang
This paper studies introducing viewpoint invariant feature representations in
existing action recognition architecture. Despite significant progress in
action recognition, efficiently handling geometric variations in large-scale
datasets remains challenging. To tackle this problem, we propose a novel
Spatial-Temporal Alignment Network (STAN), which explicitly learns geometric
invariant representations for action recognition. Notably, the STAN model is
light-weighted and generic, which could be plugged into existing action
recognition models (e.g., MViTv2) with a low extra computational cost. We test
our STAN model on widely-used datasets like UCF101 and HMDB51. The experimental
results show that the STAN model can consistently improve the state-of-the-art
models in action recognition tasks in trained-from-scratch settings.