  Recent leading approaches to semantic segmentation rely on deep convolutional
networks trained with human-annotated, pixel-level segmentation masks. Such
pixel-accurate supervision demands expensive labeling effort and limits the
performance of deep networks that usually benefit from more training data. In
this paper, we propose a method that achieves competitive accuracy but only
requires easily obtained bounding box annotations. The basic idea is to iterate
between automatically generating region proposals and training convolutional
networks. These two steps gradually recover segmentation masks for improving
the networks, and vise versa. Our method, called BoxSup, produces competitive
results supervised by boxes only, on par with strong baselines fully supervised
by masks under the same setting. By leveraging a large amount of bounding
boxes, BoxSup further unleashes the power of deep convolutional networks and
yields state-of-the-art results on PASCAL VOC 2012 and PASCAL-CONTEXT.
