  Weakly supervised semantic segmentation and localiza- tion have a problem of
focusing only on the most important parts of an image since they use only
image-level annota- tions. In this paper, we solve this problem fundamentally
via two-phase learning. Our networks are trained in two steps. In the first
step, a conventional fully convolutional network (FCN) is trained to find the
most discriminative parts of an image. In the second step, the activations on
the most salient parts are suppressed by inference conditional feedback, and
then the second learning is performed to find the area of the next most
important parts. By combining the activations of both phases, the entire
portion of the tar- get object can be captured. Our proposed training scheme is
novel and can be utilized in well-designed techniques for weakly supervised
semantic segmentation, salient region detection, and object location
prediction. Detailed experi- ments demonstrate the effectiveness of our
two-phase learn- ing in each task.
