STSM: Spatio-Temporal Shift Module for Efficient Action Recognition
This addresses the efficiency bottleneck in video action recognition for researchers and practitioners, offering a novel method to enhance 2D CNNs without added computational overhead.
The paper tackles the problem of high computational cost and large parameter size in 3D CNNs for video action recognition by proposing a plug-and-play Spatio-temporal Shift Module (STSM) that improves network performance without increasing calculations or parameters, achieving state-of-the-art results on kinetics-400 and Something-Something V2 datasets.
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.