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 …

Understanding actions in videos remains a significant challenge in computer vision, which
has been the subject of several pieces of research in the last decades. Convolutional neural …

Contrastive learning has been successfully leveraged to learn action representations for
addressing the problem of semisupervised skeleton-based action recognition. However …

Skeleton-based action recognition has rapidly become one of the most popular and
essential research topics in computer vision. The task is to analyze the characteristics of …

Vision transformers are emerging as a powerful tool to solve computer vision problems.
Recent techniques have also proven the efficacy of transformers beyond the image domain …

Occlusions areuniversal disruptions constantly present in the real world. Especially for
sparse representations, such as human skeletons, a few occluded points might destroy the …

This review emphasizes the critical need for accurate integration of solar energy into power
grids. It meticulously examines the advancements in transformer models for solar …

Graph convolutional networks (GCNs) have achieved promising results in skeleton-based
action recognition due to their capability in analysing irregular grids with non-Euclidean …

Human pose analysis has garnered significant attention within both the research community
and practical applications, owing to its expanding array of uses, including gaming, video …

In vision-based action recognition, spatio-temporal features from different modalities are
used for recognizing activities. Temporal modeling is a long challenge of action recognition …