Graph convolution networks (GCNs) have achieved remarkable performance in
skeleton-based action recognition. However, existing previous GCN-based methods
have relied excessively on elaborate human body priors and constructed complex
feature aggregation mechanisms, which limits the generalizability of networks.
To solve these problems, we propose a novel Spatial Topology Gating Unit
(STGU), which is an MLP-based variant without extra priors, to capture the
co-occurrence topology features that encode the spatial dependency across all
joints. In STGU, to model the sample-specific and completely independent
point-wise topology attention, a new gate-based feature interaction mechanism
is introduced to activate the features point-to-point by the attention map
generated from the input. Based on the STGU, in this work, we propose the first
topology-aware MLP-based model, Ta-MLP, for skeleton-based action recognition.
In comparison with existing previous methods on three large-scale datasets,
Ta-MLP achieves competitive performance. In addition, Ta-MLP reduces the
parameters by up to 62.5% with favorable results. Compared with previous
state-of-the-art (SOAT) approaches, Ta-MLP pushes the frontier of real-time
action recognition. The code will be available at
this https URL

基于骨骼的动作识别中，提出了一种新颖的基于空间拓扑门控单元（STGU）的多层感知器（MLP）模型，通过捕捉编码所有关节之间空间依赖关系的共同出现的拓扑特征来实现对样本特定且完全独立的逐点拓扑注意力的建模，并在三个大规模数据集上取得了竞争性的性能，提高了 62.5％的参数效率，推动了实时动作识别的前沿。