Semi Automatic Annotation

Semi-automatic annotation aims to accelerate and improve the process of labeling data for machine learning, addressing the significant cost and time constraints of manual annotation. Current research focuses on leveraging large language models (LLMs) and convolutional neural networks (CNNs) to assist human annotators, improving accuracy and efficiency through techniques like active learning, prompt engineering, and the development of specialized annotation tools. This work is crucial for advancing various fields, including computer vision, natural language processing, and medical image analysis, by enabling the creation of larger, higher-quality datasets necessary for training more robust and effective AI models.