Topology-aware MLP for Skeleton-based Action Recognition
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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 state-of-the-art (SOAT) approaches, Ta-MLP pushes the frontier of real-time action recognition. The code will be available at https://github.com/BUPTSJZhang/Ta-MLP.
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