Instance Segmentation Network

Instance segmentation networks aim to identify and delineate individual objects within an image, going beyond simple object detection by providing pixel-level masks for each instance. Current research focuses on improving accuracy and efficiency, particularly in challenging scenarios like dense object packing, significant class imbalance, and occlusions, often employing architectures like Mask R-CNN, transformers, and diffusion models, along with techniques such as pseudo-depth integration and data augmentation strategies like "copy and paste." These advancements have significant implications across diverse fields, including medical image analysis (e.g., kidney biopsy assessment, cancer cell segmentation), remote sensing (e.g., ship detection), and behavioral analysis (e.g., animal tracking), enabling automated analysis and improved decision-making.