Semi Supervised Instance Segmentation

Semi-supervised instance segmentation (SSIS) aims to improve the accuracy and efficiency of instance segmentation models by leveraging both labeled and unlabeled data. Current research focuses on refining pseudo-label generation techniques to mitigate noise, particularly at object boundaries, and exploring various model architectures, including teacher-student frameworks and contrastive learning methods, to effectively utilize unlabeled data. These advancements are significant because they reduce the reliance on expensive and time-consuming manual annotation, enabling the development of more robust and scalable instance segmentation models for applications ranging from medical image analysis to astronomical source detection.