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

The performance of nighttime semantic segmentation is restricted by the poor illumination
and a lack of pixel-wise annotation, which severely limit its application in autonomous …

Semantic segmentation of nighttime images plays an equally important role as that of
daytime images in autonomous driving, but the former is much more challenging due to poor …

Autonomous driving needs to ensure all-weather safety, especially in unfavorable
environments such as night and rain. However, the current daytime-trained semantic …

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 …

In this paper, we tackle the problem of semantic segmentation for nighttime images that
plays an equally important role as that for daytime images in autonomous driving, but is also …

Most progress in semantic segmentation reports on daytime images taken under favorable
illumination conditions. We instead address the problem of semantic segmentation of …

Semantic segmentation for autonomous driving should be robust against various in-the-wild
environments. Nighttime semantic segmentation is especially challenging due to a lack of …

We address the problem of semantic nighttime image segmentation and improve the state-of-
the-art, by adapting daytime models to nighttime without using nighttime annotations …

The semantic segmentation of nighttime scenes is a challenging problem that is key to
impactful applications like self-driving cars. Yet, it has received little attention compared to its …