Domain generalization (DG) remains a significant challenge for perception based on deep
neural networks (DNN), where domain shifts occur due to lighting, weather, or geolocation …

The semantic understanding of urban scenes is one of the key components for an
autonomous driving system. Complex deep neural networks for this task require to be …

Both accuracy and efficiency are of significant importance to the task of semantic
segmentation. Existing deep FCNs suffer from heavy computations due to a series of high …

Training a deep neural network for semantic segmentation relies on pixel-level ground truth
labels for supervision. However, collecting large datasets with pixel-level annotations is very …

We propose a normalization layer for unsupervised domain adaption in semantic scene
segmentation. Normalization layers are known to improve convergence and generalization …

Semantic segmentation is widely used in the industry recently, especially in the field of
scene understanding, surveillance and autonomous driving. However, majority of current …

Semi-supervised semantic segmentation (SSSS) has been proposed to alleviate the burden
of time-consuming pixel-level manual labeling which leverages limited labeled data along …

Semantic image segmentation is a basic street scene understanding task in autonomous
driving, where each pixel in a high resolution image is categorized into a set of semantic …

Though the state-of-the architectures for semantic segmentation, such as HRNet,
demonstrate impressive accuracy, the complexity arising from their salient design choices …

Unsupervised domain adaptation, which transfers supervised knowledge from a labeled
domain to an unlabeled domain, remains a tough problem in the field of computer vision …