In the context of skeleton-based action recognition, graph convolutional networks (GCNs) have been rapidly developed, whereas convolutional neural networks (CNNs) have received less attention. One reason is that CNNs are considered poor in modeling the irregular skeleton topology. To alleviate this limitation, we propose a pure CNN architecture named Topology-aware CNN (Ta-CNN) in this paper. In particular, we develop a novel cross-channel feature augmentation module, which is a combo of map-attend-group-map operations. By applying the module to the coordinate level and the joint level subsequently, the topology feature is effectively enhanced. Notably, we theoretically prove that graph convolution is a special case of normal convolution when the joint dimension is treated as channels. This confirms that the topology modeling power of GCNs can also be implemented by using a CNN. Moreover, we creatively design a SkeletonMix strategy which mixes two persons in a unique manner and further boosts the performance. Extensive experiments are conducted on four widely used datasets, i.e. N-UCLA, SBU, NTU RGB+D and NTU RGB+D 120 to verify the effectiveness of Ta-CNN. We surpass existing CNN-based methods significantly. Compared with leading GCN-based methods, we achieve comparable performance with much less complexity in terms of the required GFLOPs and parameters.

本文提出了一种基于纯CNN架构的新型拓扑感知CNN(Ta-CNN)，通过引入跨通道特征增强模块，可以有效增强拓扑特征，证明了GCNs的拓扑模型能力也可以通过CNN来实现。同时，通过独特的SkeletonMix策略，进一步提高了性能。在四个广泛使用的数据集上进行了广泛的实验，结果表明Ta-CNN优于现有的基于CNN的方法，在GFLOPs和参数要求上明显优于GCN-based方法具有可比性的性能。

面向骨架的高效拓扑卷积神经网络用于动作识别