Benefited from the powerful ability of spatial temporal Graph Convolutional Networks (ST-GCNs), skeleton-based human action recognition has gained promising success. However, the node interaction through message propagation does not always provide complementary information. Instead, it May even produce destructive noise and thus make learned representations indistinguishable. Inevitably, the graph representation would also become over-smoothing especially when multiple GCN layers are stacked. This paper proposes spatial-temporal graph deconvolutional networks (ST-GDNs), a novel and flexible graph deconvolution technique, to alleviate this issue. At its core, this method provides a better message aggregation by removing the embedding redundancy of the input graphs from either node-wise, frame-wise or element-wise at different network layers. Extensive experiments on three current most challenging benchmarks verify that ST-GDN consistently improves the performance and largely reduce the model size on these datasets.

中文翻译：

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基于骨架的人类动作识别的时空图反卷积网络

得益于时空图卷积网络（ST-GCN）的强大功能，基于骨骼的人类动作识别获得了可喜的成就。但是，通过消息传播进行的节点交互并不总是提供补充信息。取而代之的是，它甚至可能产生破坏性的噪音，从而使学得的表征难以区分。不可避免地，图形表示也将变得过于平滑，尤其是在堆叠多个GCN层时。本文提出了一种时空图反卷积网络（ST-GDN），一种新颖而灵活的图反卷积技术，以缓解这一问题。从根本上讲，该方法通过从不同网络层的节点级，帧级或元素级删除输入图的嵌入冗余，提供了更好的消息聚合。