  The deficiency of segmentation labels is one of the main obstacles to
semantic segmentation in the wild. To alleviate this issue, we present a novel
framework that generates segmentation labels of images given their image-level
class labels. In this weakly supervised setting, trained models have been known
to segment local discriminative parts rather than the entire object area. Our
solution is to propagate such local responses to nearby areas which belong to
the same semantic entity. To this end, we propose a Deep Neural Network (DNN)
called AffinityNet that predicts semantic affinity between a pair of adjacent
image coordinates. The semantic propagation is then realized by random walk
with the affinities predicted by AffinityNet. More importantly, the supervision
employed to train AffinityNet is given by the initial discriminative part
segmentation, which is incomplete as a segmentation annotation but sufficient
for learning semantic affinities within small image areas. Thus the entire
framework relies only on image-level class labels and does not require any
extra data or annotations. On the PASCAL VOC 2012 dataset, a DNN learned with
segmentation labels generated by our method outperforms previous models trained
with the same level of supervision, and is even as competitive as those relying
on stronger supervision.
