Annotated Training Image

Annotated training images are crucial for training deep learning models in various computer vision tasks, but creating them is often expensive and time-consuming. Current research focuses on mitigating this limitation through techniques like self-supervised pre-training with diffusion models, semi-supervised learning with improved distillation methods, and even unsupervised annotation using multimodal data and clustering algorithms. These advancements aim to improve model accuracy while reducing the need for extensive manual annotation, impacting fields ranging from medical image analysis and remote sensing to industrial automation.