Vision-based human action and activity recognition has an increasing importance among
the computer vision community with applications to visual surveillance, video retrieval and …

Abstract Human Action Recognition (HAR) has remained one of the most challenging tasks
in computer vision. With the surge in data-driven methodologies, the depth modality has …

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 …

Perceiving meaningful activities in a long video sequence is a challenging problem due to
ambiguous definition ofmeaningfulness' as well as clutters in the scene. We approach this …

3D skeleton-based action recognition and motion prediction are two essential problems of
human activity understanding. In many previous works: 1) they studied two tasks separately …

In recent years, there has been a proliferation of works on human action classification from
depth sequences. These works generally present methods and/or feature representations …

Graph matching (GM) is a fundamental problem in computer science, and it plays a central
role to solve correspondence problems in computer vision. GM problems that incorporate …

Recently, skeleton-based human action recognition has attracted a lot of research attention
in the field of computer vision. Graph convolutional networks (GCNs), which model the …

Multi-person motion prediction is a challenging problem due to the dependency of motion on
both individual past movements and interactions with other people. Transformer-based …

In skeleton-based action recognition, the approaches based on graph convolutional
networks (GCN) have achieved remarkable performance by modeling spatial-temporal …