Domain Adaptive Segmentation

Domain adaptive segmentation aims to train image segmentation models that generalize well to new, unseen data distributions (domains), overcoming the limitations of traditional methods that require extensive labeled data for each domain. Current research focuses on developing techniques to bridge domain gaps using various strategies, including decoupling tasks like defogging from segmentation, leveraging auxiliary information such as depth or road skeletons to enforce topological constraints, and employing contrastive learning or energy-based models to improve feature alignment and reliability. These advancements are crucial for applications like autonomous driving (road segmentation from diverse aerial imagery), medical image analysis (organelle segmentation in electron microscopy), and remote sensing, where labeled data is scarce or expensive to obtain.