In unsupervised domain adaptation (UDA), a model trained on source data (eg synthetic) is
adapted to target data (eg real-world) without access to target annotation. Most previous …

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 …

Unsupervised domain adaptation (UDA) aims to adapt a model trained on the source
domain (eg synthetic data) to the target domain (eg real-world data) without requiring further …

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 Unsupervised Domain Adaptation (UDA) can tackle the challenge that
convolutional neural network (CNN)-based approaches for semantic segmentation heavily …

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 …

Translating images from a source domain to a target domain for learning target models is
one of the most common strategies in domain adaptive semantic segmentation (DASS) …

Unsupervised domain adaptation (UDA) aims to adapt a model of the labeled source
domain to an unlabeled target domain. Existing UDA-based semantic segmentation …