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

• URL: http://arxiv.org/abs/2308.16018v4
• Date: Mon, 8 Apr 2024 14:09:27 GMT
• Title: SiT-MLP: A Simple MLP with Point-wise Topology Feature Learning for Skeleton-based Action Recognition
• Authors: Shaojie Zhang, Jianqin Yin, Yonghao Dang, Jiajun Fu,
• Abstract summary: Graph networks (GCNs) have achieved remarkable performance in skeleton-based action recognition. Previous GCN-based methods rely on elaborate human priors excessively and construct complex feature aggregation mechanisms. We propose a novel model, SiT-MLP, for skeleton-based action recognition in this work.
• Score: 9.673505408890435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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