Coarse Segmentation

Coarse segmentation is a crucial step in many computer vision tasks, aiming to create a rough initial outline of objects or regions of interest within an image or video before finer-grained segmentation. Current research emphasizes leveraging deep learning, particularly transformer-based architectures and foundation models like Segment Anything Model (SAM), often incorporating multi-stage approaches that refine initial coarse masks using additional cues or model ensembles. This technique is vital for improving efficiency and accuracy in various applications, including medical image analysis (e.g., organ segmentation, lesion detection), autonomous driving (e.g., scene understanding), and remote sensing (e.g., object identification).