SiT-MLP: A Simple MLP with Point-wise Topology Feature Learning for Skeleton-based Action Recognition

arXiv:2308.16018v4 Announce Type: replace
Abstract: 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 …

action recognition arxiv cs.cv feature mlp recognition simple topology type wise