  Skeleton-based action recognition methods are limited by the semantic
extraction of spatio-temporal skeletal maps. However, current methods have
difficulty in effectively combining features from both temporal and spatial
graph dimensions and tend to be thick on one side and thin on the other. In
this paper, we propose a Temporal-Channel Aggregation Graph Convolutional
Networks (TCA-GCN) to learn spatial and temporal topologies dynamically and
efficiently aggregate topological features in different temporal and channel
dimensions for skeleton-based action recognition. We use the Temporal
Aggregation module to learn temporal dimensional features and the Channel
Aggregation module to efficiently combine spatial dynamic channel-wise
topological features with temporal dynamic topological features. In addition,
we extract multi-scale skeletal features on temporal modeling and fuse them
with an attention mechanism. Extensive experiments show that our model results
outperform state-of-the-art methods on the NTU RGB+D, NTU RGB+D 120, and
NW-UCLA datasets.
