Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer
vision applications, providing key information for the global understanding of an image. This …

Semantic segmentation plays a fundamental role in a broad variety of computer vision
applications, providing key information for the global understanding of an image. Yet, the …

In this work, we introduce a new concept, named source-free open compound domain
adaptation (SF-OCDA), and study it in semantic segmentation. SF-OCDA is more …

This paper considers the synthetic-to-real adaptation of point cloud semantic segmentation,
which aims to segment the real-world point clouds with only synthetic labels available …

Scene appearance changes drastically throughout the day. Existing semantic segmentation
methods mainly focus on well-lit daytime scenarios and are not well designed to cope with …

Unsupervised domain adaptation (UDA) in semantic segmentation transfers the knowledge
of the source domain to the target one to improve the adaptability of the segmentation model …

Many methods of semantic image segmentation have borrowed the success of open
compound domain adaptation. They minimize the style gap between the images of source …

Deep object detection models trained on clean images may not generalize well on
degraded images due to the well-known domain shift issue. This hinders their application in …

Few shot semantic segmentation has been proposed to enhance the generalization ability of
traditional models with limited data. Previous works mainly focus on the supervised tasks …

Deep neural networks (DNNs) have proven explicit contributions in making autonomous
driving cars and related tasks such as semantic segmentation, motion tracking, object …