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 …

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 …

In the field of Transfer Learning, Source-Free Unsupervised Domain Adaptation (SFUDA)
emerges as a practical and novel task that enables a pre-trained model to adapt to a new …

Source-free object detection (SFOD) aims to transfer a detector pre-trained on a label-rich
source domain to an unlabeled target domain without seeing source data. While most …

Unsupervised domain adaptation aims to transfer knowledge from a fully-labeled source
domain to an unlabeled target domain. However, in real-world scenarios, providing …

The success of large language models has inspired the computer vision community to
explore image segmentation foundation model that is able to zero/few-shot generalize …

Test-time adaptation (TTA) aims to address the distribution shift between the training and
test data with only unlabeled data at test time. Existing TTA methods often focus on …

Abstract Model adaptation aims at solving the domain transfer problem under the constraint
of only accessing the pretrained source models. With the increasing considerations of data …

Existing domain adaptation methods usually perform explicit representation alignment by
simultaneously accessing the source data and target data. However, the source data are not …