Pseudo Label Refinement

Pseudo-label refinement focuses on improving the accuracy of automatically generated labels for unlabeled data, a crucial step in semi-supervised and unsupervised learning. Current research emphasizes strategies like incorporating contextual information (e.g., using neighboring pixel relationships or incorporating text descriptions), leveraging advanced models such as the Segment Anything Model for precise boundary detection, and employing techniques such as clustering and consistency regularization to reduce noise in pseudo-labels. These advancements enable more effective training with limited labeled data, leading to improved performance in various applications including image segmentation, object detection, and domain adaptation, ultimately reducing the reliance on extensive manual annotation.