  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.
