Performing prescribed physical exercises during home-based rehabilitation programs plays
an important role in regaining muscle strength and improving balance for people with …

Post-stroke therapy restores lost skills. Traditionally, patients are supported by skilled
therapists who monitor their progress and evaluate the program's effectiveness. Due to a …

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 …

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 …

Abstract Human Action Recognition (HAR) is a popular area of research in computer vision
due to its wide range of applications such as surveillance, health care, and gaming, etc …

This paper strives for self-supervised learning of a feature space suitable for skeleton-based
action recognition. Our proposal is built upon learning invariances to input skeleton …

Extracting effective spatial-temporal information is significantly important for video-based
action recognition. Recently 3D convolutional neural networks (3D CNNs) that could …

Graph convolution networks (GCN) have been widely used in skeleton-based action
recognition. We note that existing GCN-based approaches primarily rely on prescribed …

Video moment retrieval has progressed significantly over the past few years, aiming to
search the moment that is most relevant to a given natural language query. Most existing …

In skeleton-based action recognition, it has been a dominant paradigm to extract motion
features with temporal convolution and model spatial correlations with graph convolution …