Graph convolutional networks (GCNs) generalize convolutional neural networks into irregular graph-like structures. Generally, graph topologies are set by hand and fixed over all layers. Handcrafted connections may not be optimal and cannot fully use the self-learning ability of deep learning. In this work, we explore a topology-learnable graph convolution for skeleton-based action recognition. Specifically, a spatial graph convolution can be decomposed into a feature learning component that evolves the features of each graph vertex, and a graph vertex fusion component in which the latent graph topologies can be learned adaptively. Different initialization strategies for the learnable fusion matrix are evaluated. Experimental results that are based on the spatial-temporal GCNs for skeleton-based action recognition, demonstrate that convolution can work on graphs like on images, even if only a specific fusion matrix initialization that uses adjacency matrices is applied. Moreover, the self-learning process can learn the latent topology of a graph beyond the handcrafted topology, thereby making graph convolution flexible and universal.

图卷积网络（GCN）将卷积神经网络概括为不规则的图状结构。通常，图拓扑是手动设置并固定在所有层上的。手工连接可能不是最佳的，并且无法充分利用深度学习的自学习能力。在这项工作中，我们探索了一种基于拓扑的图卷积，用于基于骨骼的动作识别。具体而言，空间图卷积可以分解为演化每个图顶点的特征的特征学习组件，以及其中可以自适应地学习潜图拓扑的图顶点融合组件。评估了可学习融合矩阵的不同初始化策略。基于时空GCN进行基于骨骼的动作识别的实验结果，证明即使只应用了使用邻接矩阵的特定融合矩阵初始化，卷积也可以像处理图像一样作用于图上。此外，自学习过程还可以学习图的潜在拓扑，而不是手工绘制的拓扑，从而使图卷积变得灵活和通用。