  Graph convolution networks (GCNs) have achieved remarkable performance in
skeleton-based action recognition. However, previous GCN-based methods rely on
elaborate human priors excessively and construct complex feature aggregation
mechanisms, which limits the generalizability and effectiveness of networks. To
solve these problems, we propose a novel Spatial Topology Gating Unit (STGU),
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 learn the point-wise topology features, a new gate-based feature
interaction mechanism is introduced to activate the features point-to-point by
the attention map generated from the input sample. Based on the STGU, we
propose the first MLP-based model, SiT-MLP, for skeleton-based action
recognition in this work. Compared with previous methods on three large-scale
datasets, SiT-MLP achieves competitive performance. In addition, SiT-MLP
reduces the parameters significantly with favorable results. The code will be
available at https://github.com/BUPTSJZhang/SiT?MLP.
