Image Level Label

Image-level labeling in semantic segmentation aims to train accurate pixel-wise image segmentation models using only image-level class labels, significantly reducing the need for expensive pixel-level annotations. Current research focuses on improving the quality of pseudo-masks generated from these weak labels, employing techniques like ensemble methods, contrastive learning, and transformer-based architectures to better discriminate foreground objects from the background and handle ambiguous regions. This approach holds significant importance for various applications, including medical image analysis and remote sensing, where obtaining detailed annotations is challenging or impractical, enabling more efficient and scalable training of segmentation models.