Dense Pseudo Label

Dense pseudo-labeling is a semi-supervised learning technique that leverages readily available unlabeled data to improve the performance of various computer vision tasks, such as object detection and semantic segmentation. Current research focuses on refining pseudo-label generation methods, often employing teacher-student architectures and incorporating strategies like density-guided selection or dynamic thresholding to enhance the quality and reliability of these pseudo-labels. This approach is particularly valuable in scenarios with limited labeled data, leading to improved model accuracy and efficiency across diverse applications, including medical image analysis and aerial imagery interpretation.