  The modeling, computational cost, and accuracy of traditional Spatio-temporal
networks are the three most concentrated research topics in video action
recognition. The traditional 2D convolution has a low computational cost, but
it cannot capture the time relationship; the convolutional neural networks
(CNNs) model based on 3D convolution can obtain good performance, but its
computational cost is high, and the amount of parameters is large. In this
paper, we propose a plug-and-play Spatio-temporal Shift Module (STSM), which is
a generic module that is both effective and high-performance. Specifically,
after STSM is inserted into other networks, the performance of the network can
be improved without increasing the number of calculations and parameters. In
particular, when the network is 2D CNNs, our STSM module allows the network to
learn efficient Spatio-temporal features. We conducted extensive evaluations of
the proposed module, conducted numerous experiments to study its effectiveness
in video action recognition, and achieved state-of-the-art results on the
kinetics-400 and Something-Something V2 datasets.
