Annotation Efficient

Annotation-efficient learning aims to train accurate machine learning models with minimal human annotation, addressing the significant cost and time constraints of data labeling. Current research focuses on active learning strategies that intelligently select the most informative data points for annotation, often employing techniques like superpixel grouping and uncertainty sampling, and leveraging pre-trained models or multi-task learning frameworks. These advancements are crucial for various applications, including medical image analysis, remote sensing, and computer-assisted surgery, enabling the development and deployment of powerful AI systems in data-scarce scenarios.