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 …

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 …

Transformer models have shown great success handling long-range interactions, making
them a promising tool for modeling video. However, they lack inductive biases and scale …

Gait recognition is beneficial for a variety of applications, including video surveillance, crime
scene investigation, and social security, to mention a few. However, gait recognition often …

Abstract Single Image Super-Resolution (SISR) is a crucial task in low-level computer vision
aiming to reconstruct high-resolution images from low-resolution counterparts. Conventional …

Abstract Video-based Unsupervised Domain Adaptation (VUDA) method improves the
generalization of the video model enabling it to be applied to action recognition tasks in …

Although action recognition systems can achieve top performance when evaluated on in-
distribution test points, they are vulnerable to unanticipated distribution shifts in test data …

Abstract Temporal Action Detection (TAD) aims to accurately capture each action interval in
an untrimmed video and to understand human actions. This paper comprehensively surveys …

Skeleton-based methods have made remarkable strides in human action recognition (HAR).
However, the performance of existing unimodal approaches is still limited by the lack of …

Micro-expression recognition is one of the most challenging topics in affective computing. It
aims to recognize tiny facial movements difficult for humans to perceive in a brief period, ie …