Scarce Annotation

Scarce annotation, the limited availability of labeled data for training machine learning models, is a significant challenge across diverse fields like medical image segmentation, animal pose estimation, and biodiversity analysis. Current research focuses on developing annotation-efficient learning paradigms, including semi-supervised and active learning techniques, often employing deep learning architectures such as U-Net and transformers, sometimes incorporating pseudo-labeling and consistency constraints to leverage unlabeled data. Overcoming this data scarcity is crucial for advancing AI applications in these areas, enabling more accurate and robust models even with limited resources. The development of effective strategies for handling scarce annotation is therefore a key area of ongoing research with broad practical implications.