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 …

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 …

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 …

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

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 …

While transformers have greatly boosted performance in semantic segmentation, domain
adaptive transformers are not yet well explored. We identify that the domain gap can cause …

Deep neural networks (DNNs) have proven their capabilities in the past years and play a
significant role in environment perception for the challenging application of automated …

The unsupervised domain adaptation semantic segmentation task is challenging due to the
distribution shift problem between the source and the target domains. In this paper, we …

Deep learning-based solutions for semantic segmentation suffer from significant
performance degradation when tested on data with different characteristics than what was …

Supervised semantic segmentation often fails to generalize well in unseen scenarios due to
the domain gap between the source and the target domains. Unsupervised domain …