The world we see is ever-changing and it always changes with people, things, and the
environment. Domain is referred to as the state of the world at a certain moment. A research …

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 …

We propose an approach to domain adaptation for semantic segmentation that is both
practical and highly accurate. In contrast to previous work, we abandon the use of …

While self-training has advanced semi-supervised semantic segmentation, it severely suffers
from the long-tailed class distribution on real-world semantic segmentation datasets that …

Unsupervised domain adaptation (DA) has gained substantial interest in semantic
segmentation. However, almost all prior arts assume concurrent access to both labeled …

Generalized few-shot semantic segmentation (GFSS) distinguishes pixels of base and novel
classes from the background simultaneously, conditioning on sufficient data of base classes …

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 …

Abstract Domain adaptive semantic segmentation aims to adapt a model trained on labeled
source domain to the unlabeled target domain. Self-training shows competitive potential in …

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 …

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