What's the Point: Semantic Segmentation with Point Supervision
The semantic image segmentation task presents a trade off between test time accuracy and training time annotation cost. Detailed per pixel annotations enable training accurate models but are very time consuming to obtain; image level class labels are an order of magnitude cheaper but result in less accurate models. We take a natural step from image level annotation towards stronger supervision: we ask annotators to point to an object if one exists. We incorporate this point supervision along with a novel objectness potential in the training loss function of a CNN model. Experimental results on the PASCAL VOC 2012 benchmark reveal that the combined effect of point level supervision and objectness potential yields an improvement of (12.9\ mIOU over image level supervision. Further, we demonstrate that models trained with point level supervision are more accurate than models trained with image level, squiggle level or full supervision given a fixed annotation budget.
ECCV