  Skeleton-based action recognition has become popular in recent years due to
its efficiency and robustness. Most current methods adopt graph convolutional
network (GCN) for topology modeling, but GCN-based methods are limited in
long-distance correlation modeling and generalizability. In contrast, the
potential of convolutional neural network (CNN) for topology modeling has not
been fully explored. In this paper, we propose a novel CNN architecture,
Temporal-Channel Topology Enhanced Network (TCTE-Net), to learn spatial and
temporal topologies for skeleton-based action recognition. The TCTE-Net
consists of two modules: the Temporal-Channel Focus module, which learns a
temporal-channel focus matrix to identify the most critical feature
representations, and the Dynamic Channel Topology Attention module, which
dynamically learns spatial topological features, and fuses them with an
attention mechanism to model long-distance channel-wise topology. We conduct
experiments on NTU RGB+D, NTU RGB+D 120, and FineGym datasets. TCTE-Net shows
state-of-the-art performance compared to CNN-based methods and achieves
superior performance compared to GCN-based methods. The code is available at
https://github.com/aikuniverse/TCTE-Net.
