Human activity recognition (HAR) has multifaceted applications due to its worldly usage of
acquisition devices such as smartphones, video cameras, and its ability to capture human …

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 …

Human skeleton-based action recognition offers a valuable means to understand the
intricacies of human behavior because it can handle the complex relationships between …

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

Human skeleton, as a compact representation of human action, has received increasing
attention in recent years. Many skeleton-based action recognition methods adopt GCNs to …

Assembly101 is a new procedural activity dataset featuring 4321 videos of people
assembling and disassembling 101" take-apart" toy vehicles. Participants work without fixed …

One essential problem in skeleton-based action recognition is how to extract discriminative
features over all skeleton joints. However, the complexity of the recent State-Of-The-Art …

Human motion prediction is a challenging task due to the stochasticity and aperiodicity of
future poses. Recently, graph convolutional network has been proven to be very effective to …

Human action recognition aims at classifying the category of human action from a segment
of a video. Recently, people have dived into designing GCN-based models to extract …

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