Sparse Annotation

Sparse annotation focuses on training machine learning models, particularly in image and video segmentation, object detection, and other computer vision tasks, using significantly fewer labels than traditional methods. Current research emphasizes developing robust algorithms and model architectures, such as those incorporating transformers, convolutional neural networks, and Markov models, to effectively leverage limited annotations, often employing techniques like pseudo-label generation, consistency regularization, and self-supervised learning. This approach is crucial for reducing the high cost and time associated with data annotation, thereby accelerating progress in various fields including medical image analysis, remote sensing, and autonomous driving. The resulting models offer the potential for improved efficiency and accessibility in numerous applications.