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 …

Unsupervised domain adaptation (UDA) approaches focus on adapting models trained on a
labeled source domain to an unlabeled target domain. In contrast to UDA, source-free …

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) …

Semantic segmentation usually suffers from a long tail data distribution. Due to the
imbalanced number of samples across categories, the features of those tail classes may get …

In unsupervised domain adaptation (UDA), directly adapting from the source to the target
domain usually suffers significant discrepancies and leads to insufficient alignment. Thus …

Black-box unsupervised domain adaptation (UDA) learns with source predictions of target
data without accessing either source data or source models during training, and it has clear …

Self-training has shown great potential in semi-supervised learning. Its core idea is to use
the model learned on labeled data to generate pseudo-labels for unlabeled samples, and in …

Abstract Domain adaptive semantic segmentation aims to adapt a model trained on labeled
source domain to the unlabeled target domain. Self-training shows competitive potential in …

Semi-supervised learning has attracted increasing attention in the large-scale land cover
mapping task. However, existing methods overlook the potential to alleviate the class …

Unsupervised domain adaptation (UDA) in semantic segmentation transfers the knowledge
of the source domain to the target one to improve the adaptability of the segmentation model …