Enhancing the generalization capability of deep neural networks to unseen domains is
crucial for safety-critical applications in the real world such as autonomous driving. To …

This paper exploits the intrinsic features of urban-scene images and proposes a general add-
on module, called height-driven attention networks (HANet), for improving semantic …

Despite the recent success of deep-learning based semantic segmentation, deploying a pre-
trained road scene segmenter to a city whose images are not presented in the training set …

In recent years, considerable progress has been made in semantic segmentation of images
with favorable environments. However, the environmental perception of autonomous driving …

Level 5 autonomy for self-driving cars requires a robust visual perception system that can
parse input images under any visual condition. However, existing semantic segmentation …

When designing a semantic segmentation module for a practical application, such as
autonomous driving, it is crucial to understand the robustness of the module with respect to a …

Deep models trained on source domain lack generalization when evaluated on unseen
target domains with different data distributions. The problem becomes even more …

Domain-generalized urban-scene semantic segmentation (USSS) aims to learn generalized
semantic predictions across diverse urban-scene styles. Unlike generic domain gap …

Semantic segmentation labels are expensive and time consuming to acquire. To improve
label efficiency of semantic segmentation models, we revisit denoising autoencoders and …

Semantic segmentation is a critical component for street understanding task in autonomous
driving field. Existing various methods either focus on constructing the object's inner …