Reviews: Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior

The major issues of this paper are related to the motivation. Though it claims in L50 that this is the first end-to-end trainable algorithm that learns the instance segmentation model using bounding box annotations, this does not explain well the value of such problem. If the motivation of using bounding box annotation for training instance segmentation is that such bounding box is cheaper than boundary annotation, then there should be a study of performance versus annotation effort, e.g., in terms of annotation expense or total annotation time. This will answer if weakly supervised instance segmentation achieves better performance than fully supervised on given the same amount of annotation time/money. It may also be possible that given the same amount of time/money, the fine pixel annotation is better than coarse bounding box annotation in terms of training.