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 …

Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles
(CAV), including perception, planning, and control. However, its reliance on vehicular data …

Deep neural networks based purely on attention have been successful across several
domains, relying on minimal architectural priors from the designer. In Human Action …

Human behavior understanding with unmanned aerial vehicles (UAVs) is of great
significance for a wide range of applications, which simultaneously brings an urgent …

Driver distraction has become a significant cause of severe traffic accidents over the past
decade. Despite the growing development of vision-driven driver monitoring systems, the …

Semi-supervised learning has a great potential in medical image segmentation tasks with a
few labeled data, but most of them only consider single-modal data. The excellent …

Despite the impressive achievements of Deep Neural Networks (DNNs) in computer vision,
their vulnerability to adversarial attacks remains a critical concern. Extensive research has …

The availability of a large labelled dataset is a key requirement for applying deep learning
methods to solve various computer vision tasks. In the context of understanding human …

Vision is the richest and most cost-effective technology for Driver Monitoring Systems (DMS),
especially after the recent success of Deep Learning (DL) methods. The lack of sufficiently …

Autonomous vehicles (AVs) are emerging in the automobile industry with potential benefits
to reduce traffic congestion, improve mobility and accessibility, as well as safety. According …