Semi Supervised Instance Segmentation

Semi-supervised instance segmentation is a technique in the field of computer vision aimed at achieving accurate instance-level object recognition and segmentation using a small amount of labeled data and a large amount of unlabeled data. This method enhances the model's generalization ability and segmentation accuracy in data-scarce scenarios by leveraging the strengths of both supervised and unsupervised learning, making it highly valuable, especially in applications such as medical image analysis, autonomous driving, and security surveillance.