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

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 …

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 …

Action recognition based on skeleton key joints has gained popularity due to its cost
effectiveness and low complexity. Existing Convolutional Neural Network (CNN) based …

Graph convolutional networks (GCNs) are widely adopted in skeleton-based action
recognition due to their powerful ability to model data topology. We argue that the …

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 …