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Weakly supervised image segmentation trained with image-level labels usuallysuffers from inaccurate coverage of object areas during the generation of thepseudo groundtruth. This is because the object activation maps are trained withthe classification objective and lack the ability to generalize. To improve thegenerality of the objective activation maps, we propose a region prototypicalnetwork RPNet to explore the cross-image object diversity of the training set.Similar object parts across images are identified via region featurecomparison. Object confidence is propagated between regions to discover newobject areas while background regions are suppressed. Experiments show that theproposed method generates more complete and accurate pseudo object masks, whileachieving state-of-the-art performance on PASCAL VOC 2012 and MS COCO. Inaddition, we investigate the robustness of the proposed method on reducedtraining sets.

Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised Segmentation