  Transformers have become the primary backbone of the computer vision
community due to their impressive performance. However, the unfriendly
computation cost impedes their potential in the video recognition domain. To
optimize the speed-accuracy trade-off, we propose Semantic-aware Temporal
Accumulation score (STA) to prune spatio-temporal tokens integrally. STA score
considers two critical factors: temporal redundancy and semantic importance.
The former depicts a specific region based on whether it is a new occurrence or
a seen entity by aggregating token-to-token similarity in consecutive frames
while the latter evaluates each token based on its contribution to the overall
prediction. As a result, tokens with higher scores of STA carry more temporal
redundancy as well as lower semantics thus being pruned. Based on the STA
score, we are able to progressively prune the tokens without introducing any
additional parameters or requiring further re-training. We directly apply the
STA module to off-the-shelf ViT and VideoSwin backbones, and the empirical
results on Kinetics-400 and Something-Something V2 achieve over 30% computation
reduction with a negligible ~0.2% accuracy drop. The code is released at
https://github.com/Mark12Ding/STA.
