Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …

Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer
vision applications, providing key information for the global understanding of an image. This …

Unsupervised domain adaptation in semantic segmentation alleviates the reliance on
expensive pixel-wise annotation. It uses a labeled source domain dataset as well as …

Unsupervised domain adaptation (DA) with the aid of pseudo labeling techniques has
emerged as a crucial approach for domain-adaptive 3D object detection. While effective …

Test-time adaptation (TTA) aims to adapt a pre-trained model to the target domain in a batch-
by-batch manner during inference. While label distributions often exhibit imbalances in real …

Despite the emergence of principled methods for domain adaptation under label shift, their
sensitivity to shifts in class conditional distributions is precariously under explored …

Semantic segmentation plays a fundamental role in a broad variety of computer vision
applications, providing key information for the global understanding of an image. Yet, the …

The waive of labels in the target domain makes Unsupervised Domain Adaptation (UDA) an
attractive technique in many real-world applications, though it also brings great challenges …

Graph Neural Networks (GNNs) have garnered considerable interest due to their
exceptional performance in a wide range of graph machine learning tasks. Nevertheless, the …

Abstract Domain Adaptation (DA) is always challenged by the spurious correlation between
the domain-invariant features (eg, class identity) and the domain-specific ones (eg …