Visual segmentation seeks to partition images, video frames, or point clouds into multiple
segments or groups. This technique has numerous real-world applications, such as …

Unsupervised domain adaptation (UDA) aims to adapt models learned from a well-
annotated source domain to a target domain, where only unlabeled samples are given …

The annotations used during the training process are crucial for the inference results of
remote sensing images (RSIs) based on a deep learning framework. Unlabeled RSIs can be …

Most nighttime semantic segmentation studies are based on domain adaptation approaches
and image input. However, limited by the low dynamic range of conventional cameras …

In semi-supervised semantic segmentation, a model is trained with a limited number of
labeled images along with a large corpus of unlabeled images to reduce the high annotation …

Autonomous driving techniques have been flourishing in recent years while thirsting for
huge amounts of high-quality data. However, it is difficult for real-world datasets to keep up …

Unsupervised Domain Adaptation (UDA) aims to enhance the generalization of the learned
model to other domains. The domain-invariant knowledge is transferred from the model …

Unsupervised domain adaptation (UDA) and domain generalization (DG) enable machine
learning models trained on a source domain to perform well on unlabeled or even unseen …

Prototypical contrastive learning (PCL) has been widely used to learn class-wise domain-
invariant features for domain generalization semantic segmentation. These methods …

We study unsupervised domain adaptation (UDA) for semantic segmentation. Currently, a
popular UDA framework lies in self-training which endows the model with two-fold …