Graph convolutional networks (GCN) have recently been studied to exploit the graph topology of the human body for skeleton-based action recognition. However, most of these methods unfortunately aggregate messages via an inflexible pattern for various action samples, lacking the awareness of intra-class variety and the suitableness for skeleton sequences, which often contain redundant or even detrimental connections. In this paper, we propose a novel Deformable Graph Convolutional Network (DeGCN) to adaptively capture the most informative joints. The proposed DeGCN learns the deformable sampling locations on both spatial and temporal graphs, enabling the model to perceive discriminative receptive fields. Notably, considering human action is inherently continuous, the corresponding temporal features are defined in a continuous latent space. Furthermore, we design an innovative multi-branch framework, which not only strikes a better trade-off between accuracy and model size, but also elevates the effect of ensemble between the joint and bone modalities remarkably. Extensive experiments show that our proposed method achieves state-of-the-art performances on three widely used datasets, NTU RGB+D, NTU RGB+D 120, and NW-UCLA.

中文翻译：

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DeGCN：用于基于骨架的动作识别的可变形图卷积网络

最近研究了图卷积网络（GCN），以利用人体的图拓扑进行基于骨架的动作识别。然而，不幸的是，这些方法中的大多数通过各种动作样本的不灵活模式来聚合消息，缺乏对类内多样性的认识以及对骨架序列的适用性，而骨架序列通常包含冗余甚至有害的连接。在本文中，我们提出了一种新颖的可变形图卷积网络（DeGCN）来自适应捕获信息最丰富的关节。所提出的 DeGCN 学习空间图和时间图上的可变形采样位置，使模型能够感知判别感受野。值得注意的是，考虑到人类行为本质上是连续的，相应的时间特征是在连续的潜在空间中定义的。此外，我们设计了一种创新的多分支框架，不仅在精度和模型大小之间取得了更好的权衡，而且还显着提高了关节和骨骼模态之间的集成效果。大量实验表明，我们提出的方法在三个广泛使用的数据集 NTU RGB+D、NTU RGB+D 120 和 NW-UCLA 上实现了最先进的性能。