Weakly supervised segmentation

Weakly Supervised Segmentation (WSS) is an image segmentation technique in the field of computer vision. Its goal is to train models on datasets with limited or incomplete annotations to achieve accurate localization and classification of objects within images. This method effectively reduces annotation costs by leveraging partial annotation information, such as image-level labels or rough bounding boxes, while maintaining high segmentation accuracy. WSS has significant application value in medical image analysis, remote sensing image processing, and autonomous driving, among other scenarios, significantly enhancing data utilization efficiency and model generalization capabilities.