Autonomous vehicles utilize urban scene segmentation to understand the real world like a
human and react accordingly. Semantic segmentation of normal scenes has experienced a …

Unsupervised Domain Adaptive Nighttime Semantic Segmentation (UDA-NSS) aims to
adapt a robust model from a labeled daytime domain to an unlabeled nighttime domain …

Nighttime semantic segmentation is essential for various applications, eg, autonomous
driving, which often faces challenges due to poor illumination and the lack of well-annotated …

In the field of autonomous driving technology, both daytime and nighttime scenes are
common. However, due to the poor illumination and difficulty in manual annotation of …

In autonomous driving, it is crucial to train a segmentation model that can perform well in
poor illumination. Due to the lack of pixel-level annotated nighttime images and a large …

Among the existing nighttime domain adaptation methods, the semantic consistency of the
same features is largely ignored in the process of semantic alignment, which leads to …