Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well
on another set of data (target domain), which is different but has similar properties as the …

An increasing availability of remote sensing data in the era of geo big-data makes producing
well-represented, reliable training data to be more challenging and requires an excessive …

Semantic segmentation has witnessed tremendous progress due to the proposal of various
advanced network architectures. However, they are extremely hungry for delicate …

Federated Learning (FL) has recently emerged as a possible way to tackle the domain shift
in real-world Semantic Segmentation (SS) without compromising the private nature of the …

Adapting a segmentation model from a labeled source domain to a target domain, where a
single unlabeled datum is available, is one of the most challenging problems in domain …

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 …

In the last decade, many deep learning models have been well trained and made a great
success in various fields of machine intelligence, especially for computer vision and natural …

Source-Free Domain Adaptation (SFDA) is becoming topical to address the challenge of
distribution shift between training and deployment data, while also relaxing the requirement …

Unsupervised domain adaptation (UDA) carries out knowledge transfer from the labeled
source domain to the unlabeled target domain. Existing feature alignment methods in UDA …

We propose a new problem formulation and a corresponding evaluation framework to
advance research on unsupervised domain adaptation for semantic image segmentation …