Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Deep learning has emerged as a powerful tool in various domains, revolutionising machine
learning research. However, one persistent challenge is the scarcity of labelled training …

Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention
for tackling domain-shift problems caused by distribution discrepancy across different …

Over the past decade, domain adaptation has become a widely studied branch of transfer
learning which aims to improve performance on target domains by leveraging knowledge …

Excavators are widely used for material handling applications in unstructured environments,
including mining and construction. Operating excavators in a real-world environment can be …

Although considerable progress has been made in semantic scene understanding under
clear weather, it is still a tough problem under adverse weather conditions, such as dense …

Sharing information between connected and autonomous vehicles (CAVs) fundamentally
improves the performance of collaborative object detection for self-driving. However, CAVs …

Autonomous vehicles clearly benefit from the expanded Field of View (FoV) of 360° sensors,
but modern semantic segmentation approaches rely heavily on annotated training data …

Various methods for Multi-Agent Reinforcement Learning (MARL) have been developed
with the assumption that agents' policies are based on accurate state information. However …

Knee osteoarthritis is a prevalent disease worldwide. The automatic segmentation of knee
tissues in magnetic resonance (MR) images has important clinical utility in assessing knee …