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 this paper we first assess and harness various Vision Foundation Models (VFMs) in the
context of Domain Generalized Semantic Segmentation (DGSS). Driven by the motivation …

To alleviate the need for large-scale pixel-wise annotations, domain adaptation for semantic
segmentation trains segmentation models on synthetic data (source) with computer …

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) for semantic segmentation aims at improving the
model performance on the unlabeled target domain by leveraging a labeled source domain …

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 …

Most prior semantic segmentation methods have been developed for day-time scenes, while
typically underperforming in night-time scenes due to insufficient and complicated lighting …

Deep neural networks (DNNs) have proven their capabilities in the past years and play a
significant role in environment perception for the challenging application of automated …

We study source-free unsupervised domain adaptation (SFUDA) for semantic segmentation
which aims to adapt a source-trained model to the target domain without accessing the …

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 …