Efficient Annotation

Efficient annotation focuses on minimizing the time and cost associated with creating high-quality labeled datasets for machine learning, crucial for training accurate models in various fields. Current research explores strategies like active learning (selecting the most informative data for annotation), leveraging large language models for automated labeling, and improving annotation workflows through tools and better instructions. These advancements are vital for accelerating progress in areas like medical image analysis, natural language processing, and remote sensing, where high-quality labeled data is often a significant bottleneck.