Bounding Box Annotation

Bounding box annotation, the process of drawing rectangular boxes around objects in images or videos, is crucial for training many computer vision models, particularly object detectors and instance segmenters. Current research focuses on reducing the cost and effort of this annotation, exploring methods like using weaker supervision (e.g., image-level labels, point annotations, or even image captions) to generate pseudo-labels or leveraging pre-trained models (like Segment Anything Model) to automate the process. This work is significant because it enables the development and deployment of computer vision systems in applications where large, fully annotated datasets are impractical or impossible to obtain, impacting fields like autonomous driving, medical image analysis, and industrial automation.