Recent progress on skeleton-based action recognition has been substantial, benefiting mostly from the explosive development of Graph Convolutional Networks (GCN). However, prevailing GCN-based methods may not effectively capture the global co-occurrence features among joints and the local spatial structure features composed of adjacent bones. They also ignore the effect of channels unrelated to action recognition on model performance. Accordingly, to address these issues, we propose a Global Co-occurrence feature and Local Spatial feature learning model (GCLS) consisting of two branches. The first branch, based on the Vertex Attention Mechanism branch (VAM-branch), captures the global co-occurrence feature of actions effectively; the second, based on the Cross-kernel Feature Fusion branch (CFF-branch), extracts local spatial structure features composed of adjacent bones and restrains the channels unrelated to action recognition. Extensive experiments on two large-scale datasets, NTU-RGB+D and Kinetics, demonstrate that GCLS achieves the best performance when compared to the mainstream approaches.

中文翻译：

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基于骨架动作识别的全局共现特征和局部空间特征学习

基于骨架的动作识别的最新进展是实质性的，主要受益于图卷积网络（GCN）的爆炸性发展。然而，流行的基于 GCN 的方法可能无法有效地捕捉关节之间的全局共现特征和由相邻骨骼组成的局部空间结构特征。他们还忽略了与动作识别无关的渠道对模型性能的影响。因此，为了解决这些问题，我们提出了一个由两个分支组成的全局共现特征和局部空间特征学习模型（GCLS）。第一个分支，基于顶点注意力机制分支（VAM-branch），有效捕捉动作的全局共现特征；第二种，基于跨内核特征融合分支（CFF-branch），提取由相邻骨骼组成的局部空间结构特征，并抑制与动作识别无关的通道。在两个大规模数据集 NTU-RGB+D 和 Kinetics 上的大量实验表明，与主流方法相比，GCLS 实现了最佳性能。