Data generation is recognized as a potent strategy for unsupervised domain adaptation
(UDA) pertaining semantic segmentation in adverse weathers. Nevertheless, these adverse …

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

While originally designed for image generation, diffusion models have recently shown to
provide excellent pretrained feature representations for semantic segmentation. Intrigued by …

LiDAR semantic segmentation (LSS) is a critical task in autonomous driving and has
achieved promising progress. However prior LSS methods are conventionally investigated …

Although recent methods in Unsupervised Domain Adaptation (UDA) have achieved
success in segmenting rainy or snowy scenes by improving consistency, they face …

Road scene understanding tasks have recently become crucial for self-driving vehicles. In
particular, real-time semantic segmentation is indispensable for intelligent self-driving …

We present a novel approach for unsupervised road segmentation in adverse weather
conditions such as rain or fog. This includes a new algorithm for source-free domain …

In smart city contexts, traditional methods for semantic segmentation are affected by adverse
conditions, such as rain, fog, or darkness. One challenge is the limited availability of …

Abstract Unsupervised Domain Adaptation (UDA) aims at reducing the domain gap between
training and testing data and is, in most cases, carried out in offline manner. However …

Abstract Unsupervised Domain Adaptation (UDA) aims to solve the problem of label scarcity
of the target domain by transferring the knowledge from the label rich source domain …