Semi Supervised Semantic Segmentation

Semi-supervised semantic segmentation aims to improve the accuracy and efficiency of image segmentation by leveraging both labeled and unlabeled data, thereby reducing the need for expensive manual annotation. Current research focuses on refining pseudo-labeling techniques, improving the reliability of predictions from unlabeled data through methods like contrastive learning, consistency regularization, and prototype-based approaches, often integrated within teacher-student frameworks or employing novel architectures like transformers. These advancements are significant because they enable the development of accurate segmentation models with limited labeled datasets, impacting various fields including medical image analysis, autonomous driving, and remote sensing.