Instance Level Segmentation

Instance-level segmentation aims to precisely delineate individual objects within an image or video, assigning a unique label to each instance. Current research emphasizes improving accuracy and efficiency, particularly for challenging scenarios like large size variations, unseen object classes (open-world segmentation), and temporal consistency in video. Transformer-based architectures and methods leveraging pre-trained models like Segment Anything Model (SAM) are prominent, alongside advancements in attention mechanisms and multi-scale processing to enhance segmentation robustness. These improvements have significant implications for diverse applications, including autonomous driving (LiDAR point cloud segmentation), material science (crystal size measurement), and document analysis (handwritten text recognition).