Domain Generalized

Domain generalized semantic segmentation (DGSS) aims to train a model on one or more source domains to accurately segment images from unseen target domains, addressing the challenge of deep learning models' sensitivity to data distribution shifts. Current research focuses on leveraging vision foundation models (VFMs) like CLIP and SAM, incorporating transformer architectures, and developing novel techniques such as style disentanglement, content-enhanced attention mechanisms, and data augmentation strategies (e.g., diffusion models, hallucination) to improve generalization. Successful DGSS methods hold significant potential for improving the robustness and applicability of semantic segmentation in real-world scenarios where training data may not fully represent the diversity of deployment environments, particularly in medical imaging and autonomous driving.