Multi Class Multi Instance Segmentation

Multi-class multi-instance segmentation (MC-MIS) aims to identify and delineate multiple instances of various object classes within a single image, a crucial task in diverse fields like medical image analysis and robotics. Current research focuses on improving the accuracy and efficiency of MC-MIS, particularly using transformer-based architectures and leveraging techniques like knowledge distillation and prompt learning to adapt pre-trained models (such as Segment Anything Model) to specific domains and datasets with limited annotations. These advancements are driving progress in applications ranging from automated plant monitoring to improved medical diagnostics, where precise object segmentation is essential for accurate analysis and decision-making.