Image Labeling

Image labeling, the process of assigning semantic meaning to image data, is crucial for training computer vision models and enabling various applications. Current research emphasizes improving labeling efficiency and accuracy through automated techniques, including leveraging large language models for collaborative human-AI annotation, employing novel methods like transferring segmentation masks from RGB to multispectral images, and developing automated labeling approaches based on pre-trained models and inductive logic learning. These advancements are significant because they address the bottleneck of manual labeling, enabling the creation of larger, higher-quality datasets for training more robust and accurate computer vision systems across diverse domains, such as medical imaging and remote sensing.