Annotation Effort

Annotation effort, the cost and time involved in labeling data for machine learning, is a major bottleneck in many areas of natural language processing and computer vision. Current research focuses on reducing this effort through techniques like active learning (strategically selecting data for annotation), leveraging pre-trained models to accelerate the process, and employing foundation models like Segment Anything Model for efficient annotation generation, even from non-experts. These advancements aim to improve the efficiency and scalability of training machine learning models, particularly in resource-intensive domains like medical image analysis and language documentation, ultimately leading to more robust and widely applicable AI systems.