Selective Annotation

Selective annotation focuses on strategically choosing which data points to label, aiming to maximize model performance while minimizing annotation costs. Current research explores various active learning techniques, prompt engineering strategies for vision-language models, and methods to handle incomplete or noisy annotations, often employing Bayesian approaches, submodular optimization, or teacher-student models. This field is crucial for advancing machine learning in data-scarce domains like medical image analysis and natural language processing, where high-quality annotations are expensive and time-consuming to obtain.