The aim of this paper is to give an overview of the recent advancements in the Unsupervised
Domain Adaptation (UDA) of deep networks for semantic segmentation. This task is …

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 …

As acquiring pixel-wise annotations of real-world images for semantic segmentation is a
costly process, a model can instead be trained with more accessible synthetic data and …

Self-training is a competitive approach in domain adaptive segmentation, which trains the
network with the pseudo labels on the target domain. However inevitably, the pseudo labels …

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 …

The divergence between labeled training data and unlabeled testing data is a significant
challenge for recent deep learning models. Unsupervised domain adaptation (UDA) …

Unsupervised domain adaptation without accessing expensive annotation processes of
target data has achieved remarkable successes in semantic segmentation. However, most …

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

Recent advances in deep learning have led to the development of accurate and efficient
models for various computer vision applications such as classification, segmentation, and …

We consider the problem of unsupervised domain adaptation for semantic segmentation by
easing the domain shift between the source domain (synthetic data) and the target domain …