Deep learning-based intelligent vehicle perception has been develo** prominently in
recent years to provide a reliable source for motion planning and decision making in …

Artificial intelligence systems are increasingly being used in industrial applications, security
and military contexts, disaster response complexes, policing and justice practices, finance …

In this paper we first assess and harness various Vision Foundation Models (VFMs) in the
context of Domain Generalized Semantic Segmentation (DGSS). Driven by the motivation …

Existing semantic segmentation methods improve generalization capability, by regularizing
various images to a canonical feature space. While this process contributes to …

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

Prototypical contrastive learning (PCL) has been widely used to learn class-wise domain-
invariant features for domain generalization semantic segmentation. These methods …

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 …

When models, eg, for semantic segmentation, are applied to images that are vastly different
from training data, the performance will drop significantly. Domain adaptation methods try to …

The rapid development of Vision Foundation Model (VFM) brings inherent out-domain
generalization for a variety of down-stream tasks. Among them, domain generalized …

Synthetic data offers the promise of cheap and bountiful training data for settings where
labeled real-world data is scarce. However, models trained on synthetic data significantly …