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 …

Recent years have seen an unprecedented spread of Unmanned Aerial Vehicles (UAVs, or
“drones”), which are highly useful for both civilian and military applications. Flight safety is a …

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 …

Abstract Domain generalization aims to learn a generalizable model from aknown'source
domain for variousunknown'target domains. It has been studied widely by domain …

Semantic segmentation models based on convolutional neural networks have recently
displayed remarkable performance for a multitude of applications. However, these models …

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

Unsupervised domain adaptation (UDA) aims to transfer the knowledge learnt from a
labeled source domain to an unlabeled target domain. Previous work is mainly built upon …

Abstract Domain adaptation for semantic segmentation aims to improve the model
performance in the presence of a distribution shift between source and target domain …

Existing cross-domain semantic segmentation methods usually focus on the overall
segmentation results of whole objects but neglect the importance of object boundaries. In …

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