Box Supervised Segmentation

Box-supervised segmentation aims to improve image segmentation accuracy using bounding boxes as weak supervision instead of precise pixel-level annotations, reducing the need for extensive manual labeling. Current research focuses on refining algorithms to handle imprecise boxes, leveraging powerful foundational models like Segment Anything Model (SAM), and incorporating techniques like monotonicity constraints to mitigate the impact of noisy box annotations. This approach holds significant promise for various applications, including medical image analysis (e.g., tumor segmentation, EMR processing) and multimodal named entity recognition, where obtaining precise pixel-level labels is challenging or expensive.