Unsupervised domain adaptation (UDA) for semantic segmentation aims to adapt a
segmentation model trained on the labeled source domain to the unlabeled target domain …

There has been a lot of effort in improving the performance of unsupervised domain
adaptation for semantic segmentation task, however there is still a huge gap in performance …

Abstract Domain adaptive semantic segmentation methods commonly utilize stage-wise
training, consisting of a warm-up and a self-training stage. However, this popular approach …

The research of semantic segmentation based on unsupervised domain adaptation greatly
alleviates the high-cost bottleneck of manual annotation in deep learning. Inevitably domain …

In this paper, we tackle the problem of one-shot unsupervised domain adaptation (OSUDA)
for semantic segmentation where the segmentors only see one unlabeled target image …

Deep\footnote learning-based semantic segmentation methods require a huge amount of
training images with pixel-level annotations. Unsupervised domain adaptation (UDA) for …

Self-training has greatly facilitated domain adaptive semantic segmentation, which iteratively
generates pseudo labels on unlabeled target data and retrains the network. However …

Utilizing synthetic data for semantic segmentation can significantly relieve human efforts in
labelling pixel-level masks. A key challenge of this task is how to alleviate the data …

Multi-source unsupervised domain adaptation (MSDA) aims at adapting models trained on
multiple labeled source domains to an unlabeled target domain. In this paper, we propose a …

This paper studies a practical domain adaptative (DA) semantic segmentation problem
where only pseudo-labeled target data is accessible through a black-box model. Due to the …