The rapid advancement of foundation models (FMs) across language, image, audio, and
video domains has shown remarkable capabilities in diverse tasks. However, the …

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

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

Deep models are notoriously known to perform poorly when encountering new domains with
different statistics. To alleviate this issue, we present a new domain generalization method …

Learning scene semantics that can be well generalized to foggy conditions is important for
safety-crucial applications such as autonomous driving. Existing methods need both …

Abstract Domain generalization semantic segmentation methods aim to generalize well on
out-of-distribution scenes, which is crucial for real-world applications. Recent works focus on …

Foggy-scene semantic segmentation (FSSS) is highly challenging due to the diverse effects
of fog on scene properties and the limited training data. Existing research has mainly …

Domain generalized semantic segmentation (DGSS) is an essential but highly challenging
task, in which the model is trained only on source data and any target data is not available …

Deep neural networks have made significant progress in various tasks under the
assumption of the same distribution between training and testing data. However, the …

Abstract Domain generalization in semantic segmentation aims to alleviate the performance
degradation on unseen domains through learning domain-invariant features. Existing …