In the skeleton-based human action recognition domain, the spatial-temporal graph convolution networks (ST-GCNs) have made great progress recently. However, they use only one fixed temporal convolution kernel, which is not enough to extract the temporal cues comprehensively. Moreover, simply connecting the spatial graph convolution layer (GCL) and the temporal GCL in series is not the optimal solution. To this end, we propose a novel enhanced spatial and extended temporal graph convolutional network (EE-GCN) in this paper. Three convolution kernels with different sizes are chosen to extract the discriminative temporal features from shorter to longer terms. The corresponding GCLs are then concatenated by a powerful yet efficient one-shot aggregation (OSA) + effective squeeze-excitation (eSE) structure. The OSA module aggregates the features from each layer once to the output, and the eSE module explores the interdependency between the channels of the output. Besides, we propose a new connection paradigm to enhance the spatial features, which expand the serial connection to a combination of serial and parallel connections by adding a spatial GCL in parallel with the temporal GCLs. The proposed method is evaluated on three large scale datasets, and the experimental results show that the performance of our method exceeds previous state-of-the-art methods.

中文翻译：

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用于基于骨架的动作识别的增强空间和扩展时间图卷积网络。


在基于骨架的人体动作识别领域，时空图卷积网络（ST-GCN）最近取得了巨大进展。然而，他们只使用一个固定的时间卷积核，这不足以全面提取时间线索。而且，简单地将空间图卷积层（GCL）和时间GCL串联起来并不是最优的解决方案。为此，我们在本文中提出了一种新颖的增强空间和扩展时间图卷积网络（EE-GCN）。选择三个不同大小的卷积核来提取从短期到长期的判别性时间特征。然后，相应的 GCL 通过强大而高效的一次性聚合 (OSA) + 有效的挤压激励 (eSE) 结构连接起来。 OSA 模块将每层的特征聚合一次到输出，eSE 模块探索输出通道之间的相互依赖性。此外，我们提出了一种新的连接范式来增强空间特征，通过添加与时间 GCL 并行的空间 GCL，将串行连接扩展为串行和并行连接的组合。所提出的方法在三个大型数据集上进行了评估，实验结果表明我们的方法的性能超过了以前最先进的方法。