Pseudo-labeling is widely applied to generate pseudo labels of target samples in most
Unsupervised Domain Adaptation (UDA) methods. Existing UDA methods designed the …

Source-free domain adaptation requires no access to the source domain training data
during unsupervised domain adaption. This is critical for meeting particular data sharing …

By reason of factors such as terrains, weather conditions, sensor imaging methods, and
cultural and economic development, there is a large shift between the remote sensing …

This review article comprehensively delves into the rapidly evolving field of domain
adaptation in computer and robotic vision. It offers a detailed technical analysis of the …

Remote sensing (RS) image semantic segmentation using deep convolutional neural
networks (DCNNs) has shown great success in various applications. However, the high …

In this paper, we study the task of source-free domain adaptation (SFDA), where the source
data are not available during target adaptation. Previous works on SFDA mainly focus on …

The application of domain adaptation techniques has emerged as a valuable approach for
reducing the cost of data annotation in object recognition domains. Despite its usefulness …

Abstract Domain generalization generalizes a prediction model trained on multiple source
domains to an unseen target domain. The source and target domains are different but …

Existing methods mainly assign pseudo labels by clustering algorithms to solve the
problems in domain adaptive pedestrian re-identification caused by unlabeled target …

Abstract Open-Set Domain Adaptation (OSDA) aims to adapt the model trained on a source
domain to the recognition tasks in a target domain while shielding any distractions caused …