3D RANs: 3D Residual Attention Networks for action recognition

In this work, we propose 3D Residual Attention Networks (3D RANs) for action recognition, which can learn spatiotemporal representation from videos. The proposed network consists of attention mechanism and 3D ResNets architecture, and it can capture spatiotemporal information in an end-to-end manner. Specifically, we separately add the attention mechanism along channel and spatial domain to each block of 3D ResNets. For each sliced tensor of an intermediate feature map, we sequentially infer channel and spatial attention maps by channel and spatial attention mechanism submodules in each residual unit block, and the attention maps are multiplied to the input feature map to reweight the key features. We validate our network through extensive experiments in UCF-101, HMDB-51 and Kinetics datasets. Our experiments show that the proposed 3D RANs are superior to the state-of-the-art approaches for action recognition, demonstrating the effectiveness of our networks.