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 …

Automated driving is an inevitable trend in future transportation, it is also one of the eminent
achievements in the matter of artificial intelligence. Deep learning produces a significant …

Unsupervised domain adaptation (UDA) for semantic segmentation aims to transfer the pixel-
wise knowledge from the labeled source domain to the unlabeled target domain. However …

Abstract Performance in Speech Emotion Recognition (SER) on a single language has
increased greatly in the last few years thanks to the use of deep learning techniques …

Semantic segmentation requires a lot of training data, which necessitates costly annotation.
There have been many studies on unsupervised domain adaptation (UDA) from one domain …

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

Abstract Unsupervised Domain Adaptation (UDA) aims to improve the generalization
capacity of models when they are tested on a real-world target domain by learning a model …

Traditional domain adaptive semantic segmentation addresses the task of adapting a model
to a novel target domain under limited or no additional supervision. While tackling the input …

We present an unsupervised adaptation approach for visual scene understanding in
unstructured traffic environments. Our method is designed for unstructured real-world …

Current unsupervised domain adaptation (UDA) methods for semantic segmentation
typically assume identical class labels between the source and target domains. This …