Promptable Segmentation

Promptable segmentation aims to segment images and videos using various prompts, such as points, boxes, or even text descriptions, guiding a model to isolate specific objects or regions of interest. Current research heavily utilizes transformer-based architectures, often adapting and extending the Segment Anything Model (SAM) and its successor, SAM 2, for applications ranging from remote sensing and medical imaging to 3D point cloud segmentation and video object tracking. This approach offers significant advantages in efficiency and generalization, particularly in scenarios with limited labeled data, impacting diverse fields by enabling faster and more accurate image analysis.