Multi Class Segmentation

Multi-class segmentation aims to assign each pixel in an image to one of several predefined classes, enabling detailed scene understanding beyond simple object detection. Current research emphasizes improving accuracy and efficiency through novel architectures like U-Net variations and transformers, often incorporating techniques such as self-attention mechanisms, bidirectional feature pyramids, and topologically-aware loss functions to address challenges like boundary delineation and class imbalance. These advancements are driving progress in diverse fields, including medical image analysis (e.g., organ segmentation, cell identification), remote sensing (e.g., land cover classification), and industrial applications (e.g., automated infrastructure inspection), where accurate and efficient segmentation is crucial for diagnosis, planning, and monitoring.

Papers