Zero Shot Segmentation

Zero-shot segmentation aims to segment images into meaningful regions without requiring any training data specific to those regions, relying instead on pre-trained models and prompts like text descriptions or bounding boxes. Current research focuses on adapting and improving foundation models such as Segment Anything Model (SAM) and its variants for various applications, including medical imaging, agricultural robotics, and 3D scene understanding, often incorporating techniques like diffusion models and optimal transport. This capability significantly reduces the need for extensive labeled datasets, accelerating progress in diverse fields and enabling more efficient and adaptable image analysis tools.