Box Annotation

Box annotation, the process of using bounding boxes to label objects in images or videos, is increasingly used to reduce the cost and time associated with creating training datasets for computer vision tasks, particularly instance segmentation. Current research focuses on developing methods to leverage these relatively inexpensive box annotations to generate high-quality pixel-level masks, often employing techniques like monotonicity constraints, pseudo-label generation, and multi-task learning within encoder-decoder architectures. This approach significantly impacts the field by enabling the training of accurate models for various applications, including medical image analysis and video understanding, with substantially less annotation effort than traditional pixel-level labeling.