Human Action Recognition (HAR) aims to understand human behavior and assign a label to
each action. It has a wide range of applications, and therefore has been attracting increasing …

Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

Graph convolutional networks (GCNs) have been widely used and achieved remarkable
results in skeleton-based action recognition. In GCNs, graph topology dominates feature …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

Action recognition with skeleton data is attracting more attention in computer vision.
Recently, graph convolutional networks (GCNs), which model the human body skeletons as …

In the semi-supervised skeleton-based action recognition task, obtaining more
discriminative information from both labeled and unlabeled data is a challenging problem …

Research on depth-based human activity analysis achieved outstanding performance and
demonstrated the effectiveness of 3D representation for action recognition. The existing …

Action recognition with skeleton data has recently attracted much attention in computer
vision. Previous studies are mostly based on fixed skeleton graphs, only capturing local …

The skeleton data have been widely used for the action recognition tasks since they can
robustly accommodate dynamic circumstances and complex backgrounds. In existing …

Graph convolutional networks (GCNs), which generalize CNNs to more generic non-
Euclidean structures, have achieved remarkable performance for skeleton-based action …