Although widely used in many applications, accurate and efficient human action recognition
remains a challenging area of research in the field of computer vision. Most recent surveys …

Human pose estimation aims at predicting the poses of human body parts in images or
videos. Since pose motions are often driven by some specific human actions, knowing the …

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

This paper presents X3D, a family of efficient video networks that progressively expand a
tiny 2D image classification architecture along multiple network axes, in space, time, width …

Human activity recognition (HAR) is a complex and multifaceted problem. The research
community has reported numerous approaches to perform HAR. Along with HAR …

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 …

In skeleton-based action recognition, graph convolutional networks (GCNs) have achieved
remarkable success. Nevertheless, how to efficiently model the spatial-temporal skeleton …

In skeleton-based action recognition, graph convolutional networks (GCNs), which model
the human body skeletons as spatiotemporal graphs, have achieved remarkable …