Mixed Attention and Channel Shift Transformer for Efficient Action Recognition

The practical use of the Transformer-based methods for processing videos is constrained by the high computing complexity. Although previous approaches adopt the spatiotemporal decomposition of 3D attention to mitigate the issue, they suffer from the drawback of neglecting the majority of visual tokens. This article presents a novel mixed attention operation that subtly fuses the random, spatial, and temporal attention mechanisms. The proposed random attention stochastically samples video tokens in a simple yet effective way, complementing other attention methods. Furthermore, since the attention operation concentrates on learning long-distance relationships, we employ the channel shift operation to encode short-term temporal characteristics. Our model can provide more comprehensive motion representations thanks to the amalgamation of these techniques. Experimental results show that the proposed method produces competitive action recognition results with low computational overhead on both large-scale and small-scale public video datasets.