Cross Domain Segmentation

Cross-domain segmentation aims to train models that accurately segment images or 3D data across different domains (e.g., different imaging modalities, geographical locations, or sensor types), overcoming the limitations of models trained on a single domain. Current research focuses on leveraging techniques like few-shot learning, domain adaptation (including unsupervised and source-free approaches), and the integration of foundation models (such as Segment Anything Model) to improve generalization and reduce the need for extensive labeled data in the target domain. This research is crucial for applications where labeled data is scarce or expensive to obtain, such as medical image analysis and remote sensing, enabling more robust and widely applicable segmentation models.