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

Semantic segmentation assigns a category for each pixel and has achieved great success in
a supervised manner. However, it fails to generalize well in new domains due to the domain …

In recent years, machine learning methods have been widely used to predict the long-term
settlement of tunnels. However, data-driven models for long-term settlement prediction often …

Video semantic segmentation is beneficial for dynamic scene processing in real-world
environments, and achieves superior performance on independent and identically …

Unsupervised cross domain semantic segmentation recently has gained much attention,
due to its powerful ability of solving the segmentation problem on unlabeled domains …

Unsupervised domain adaptative semantic segmentation is a powerful solution for the
distribution shift problem between the source and target domains. However, such methods …

The primary techniques for domain generalization in semantic segmentation revolve around
domain randomization and feature whitening. Although less commonly employed, methods …

Freespace detection (FD), also known as drivable area detection, plays a crucial role in
autonomous driving. However, existing FD methods, which are based on supervised …

Unsupervised domain adaptation semantic segmentation attracts much research attention
due to the expensive pixel-level annotation cost. Since the adaptation difficulty of samples is …

Despite the widespread use of deep neural networks in various tasks, their weak
generalization ability to out-of-distribution data has always been one of the challenges …