Weakly Supervised Instance Segmentation

Weakly supervised instance segmentation is a technique in the field of computer vision that aims to achieve precise segmentation of specific object instances in images with minimal labeled data. This method leverages image-level labels or incomplete annotations to reduce the reliance on pixel-accurate annotations, thereby lowering the cost of data labeling and improving model training efficiency. Weakly supervised instance segmentation has significant application value in medical image analysis, autonomous driving, security surveillance, and other fields, effectively enhancing the intelligence and practicality of systems.