  Weakly supervised image segmentation trained with image-level labels usually
suffers from inaccurate coverage of object areas during the generation of the
pseudo groundtruth. This is because the object activation maps are trained with
the classification objective and lack the ability to generalize. To improve the
generality of the objective activation maps, we propose a region prototypical
network RPNet to explore the cross-image object diversity of the training set.
Similar object parts across images are identified via region feature
comparison. Object confidence is propagated between regions to discover new
object areas while background regions are suppressed. Experiments show that the
proposed method generates more complete and accurate pseudo object masks, while
achieving state-of-the-art performance on PASCAL VOC 2012 and MS COCO. In
addition, we investigate the robustness of the proposed method on reduced
training sets.
