论文标题
基于骨架的人类动作识别的进行性时空图形卷积网络
Progressive Spatio-Temporal Graph Convolutional Network for Skeleton-Based Human Action Recognition
论文作者
论文摘要
图形卷积网络（GCN）在基于骨架的人类动作识别中非常成功，其中将骨骼的序列建模为图。但是，该区域中的大多数基于GCN的方法训练具有固定拓扑的深馈网络，从而导致高计算复杂性并限制其在低计算方案中的应用。在本文中，我们提出了一种方法，可以自动找到一种以渐进的方式为时空图卷积网络的紧凑和特定问题的拓扑。与基于骨架的人类作用识别的两个广泛使用的数据集的实验结果表明，与最先进的计算复杂性相比，该方法具有竞争力甚至更好的分类性能。
Graph convolutional networks (GCNs) have been very successful in skeleton-based human action recognition where the sequence of skeletons is modeled as a graph. However, most of the GCN-based methods in this area train a deep feed-forward network with a fixed topology that leads to high computational complexity and restricts their application in low computation scenarios. In this paper, we propose a method to automatically find a compact and problem-specific topology for spatio-temporal graph convolutional networks in a progressive manner. Experimental results on two widely used datasets for skeleton-based human action recognition indicate that the proposed method has competitive or even better classification performance compared to the state-of-the-art methods with much lower computational complexity.
