Shot Semantic Segmentation

Few-shot semantic segmentation aims to accurately segment images into meaningful regions using only a limited number of labeled examples per class, addressing the challenge of data scarcity in many applications. Current research focuses on improving model generalization across different domains and object types, employing architectures like transformers and diffusion models, and exploring techniques such as prototype learning, visual prompting, and cross-domain adaptation to enhance performance. This field is significant because it enables efficient training of segmentation models for tasks with limited labeled data, impacting diverse areas such as medical image analysis, autonomous driving, and remote sensing.