Semi-Supervised Semantic Segmentation

Semi-Supervised Semantic Segmentation is a task in the field of computer vision that aims to train models using a small amount of labeled data and a large amount of unlabeled data to achieve the goal of classifying each pixel in an image. This method effectively leverages unlabeled data to improve the model's generalization ability and segmentation accuracy, reducing annotation costs and enhancing the model's application value in real-world scenarios.