The goal of semantic segmentation is to segment the input image according to semantic
information and predict the semantic category of each pixel from a given label set. With the …

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 …

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 …

We describe a simple method for unsupervised domain adaptation, whereby the
discrepancy between the source and target distributions is reduced by swapping the low …

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

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

This paper focuses on the unsupervised domain adaptation of transferring the knowledge
from the source domain to the target domain in the context of semantic segmentation …

We consider the problem of unsupervised domain adaptation in semantic segmentation. The
key in this campaign consists in reducing the domain shift, ie, enforcing the data distributions …

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

Since annotating pixel-level labels for semantic segmentation is laborious, leveraging
synthetic data is an attractive solution. However, due to the domain gap between synthetic …