Learning Heterogeneous Spatial-Temporal Context for Skeleton-Based Action Recognition

• Published in: IEEE transaction on neural networks and learning systems Vol. 35; no. 9; pp. 12130 - 12141
• Main Authors: Gao, Xuehao, Yang, Yang, Wu, Yang, Du, Shaoyi
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2024
• Subjects: Context awareness; Convolutional neural networks; Feature extraction; Heterogeneous context learning; multiscale graph; Representation learning; Skeleton; skeleton-based action recognition; spatial–temporal feature representation; Spatiotemporal phenomena; Topology

Graph convolution networks (GCNs) have been widely used and achieved fruitful progress in the skeleton-based action recognition task. In GCNs, node interaction modeling dominates the context aggregation and, therefore, is crucial for a graph-based convolution kernel to extract representative feature...