One-shot semantic segmentation focuses on segmenting objects in an image using a single annotated example as a reference. This is achieved through few-shot learning techniques that train models to generalize from minimal labeled data.
Models often use a combination of feature extraction and metric learning. For instance, a convolutional neural network (CNN) extracts features from the input and reference images, and a similarity metric compares these features to segment the target object. Frameworks like PANet (Prototype Alignment Network) or FSS-1000 datasets are commonly used for one-shot segmentation tasks.
One-shot segmentation is particularly useful in medical imaging and applications where acquiring large labeled datasets is challenging. The method’s success depends on the quality of feature representation and the ability to generalize to unseen objects.