One Shot Segmentation

One-shot segmentation aims to segment images into different classes using only a single labeled example image per class, drastically reducing the annotation burden compared to traditional supervised learning. Current research focuses on leveraging powerful foundation models like Segment Anything Model (SAM), incorporating self-supervised learning techniques to learn robust representations from unlabeled data, and developing novel strategies for prompt generation and feature matching to improve segmentation accuracy. This field is significant because it promises to accelerate the development of segmentation models across various domains, particularly in medical imaging and robotics, where labeled data is scarce and expensive to acquire.