Image Level Annotation

Image-level annotation, a technique using only image-wide labels instead of pixel-level annotations, aims to reduce the substantial cost and time associated with creating large, fully labeled datasets for image analysis. Current research focuses on developing methods that effectively leverage these weak labels for tasks like object detection and semantic segmentation, employing techniques such as contrastive learning, active learning, and multiple instance learning within various deep learning architectures. This approach is significant because it enables the training of powerful models for applications such as medical image analysis and remote sensing where obtaining fully annotated data is often impractical, thereby accelerating progress in these fields.