Limited Annotation

Limited annotation research focuses on developing machine learning models that perform effectively with minimal labeled data, addressing the high cost and difficulty of obtaining extensive, accurately annotated datasets. Current research emphasizes semi-supervised and self-supervised learning techniques, often incorporating contrastive learning, active learning, and prompt engineering, alongside adaptations of models like UNet, Mask-RCNN, and Vision Transformers. These advancements are crucial for various applications, including medical image analysis, agricultural technology, and natural language processing, where acquiring large labeled datasets is impractical or prohibitively expensive. The ultimate goal is to improve model generalization and robustness while significantly reducing the annotation burden.