Semi-supervised 2D and 3D landmark labeling

"Semi-supervised 2D and 3D landmark labeling" is a computer vision technique designed to automatically or semi-automatically locate and label keypoints in images using a small amount of labeled data and a large amount of unlabeled data. This method effectively improves labeling efficiency and accuracy by combining the advantages of supervised and unsupervised learning, reducing the cost of manual annotation. Its applications are extensive, including medical image analysis, facial recognition, pose estimation, and other fields, significantly enhancing system performance and robustness.