Adaptation of semantic segmentation networks to different visual conditions is vital for robust
perception in autonomous cars and robots. However, previous work has shown that most …

Recent semantic segmentation models perform well under standard weather conditions and
sufficient illumination but struggle with adverse weather conditions and nighttime. Collecting …

This work investigates learning pixel-wise semantic image segmentation in urban scenes
without any manual annotation, just from the raw non-curated data collected by cars which …

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 …

The performance of nighttime semantic segmentation has been significantly improved
thanks to recent unsupervised methods. However, these methods still suffer from complex …

The recent advances in deep neural networks have convincingly demonstrated high
capability in learning vision models on large datasets. Nevertheless, collecting expert …

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

In autonomous driving, learning a segmentation model that can adapt to various
environmental conditions is crucial. In particular, copying with severe illumination changes is …

Comprehensive semantic segmentation is one of the key components for robust scene
understanding and a requirement to enable autonomous driving. Driven by large scale …