Human action recognition and posture prediction aim to recognize and predict respectively
the action and postures of persons in videos. They are both active research topics in …

Skeleton-based gesture recognition methods have achieved high success using Graph
Convolutional Network (GCN), which commonly uses an adjacency matrix to model the …

Accurate and timely fault diagnosis is of great significance for the safe operation and power
supply reliability of distribution systems. However, traditional intelligent methods limit the use …

In the task of skeleton-based action recognition, long-term temporal dependencies are
significant cues for sequential skeleton data. State-of-the-art methods rarely have access to …

Human action recognition has been applied in many fields, such as video surveillance and
human computer interaction, where it helps to improve performance. Numerous reviews of …

The skeletal data has been an alternative for the human action recognition task as it
provides more compact and distinct information compared to the traditional RGB input …

Skeleton-based action recognition has been substantially driven by the development of
artificial intelligence technology and deep sensors. Recently, graph convolutional networks …

Abstract Graph Convolutional Network (GCN) has already been successfully applied to
skeleton-based action recognition. However, current GCNs in this task are lack of pooling …

One of the major challenges that deaf people face in modern societal life is communication.
For those engaged in agricultural jobs, efficiency at work and productivity are deeply related …

Abstract Graph Convolutional Networks (GCNs) have become the standard skeleton-based
human action recognition research paradigm. As a core component in graph convolutional …