  Compared with expensive pixel-wise annotations, image-level labels make it
possible to learn semantic segmentation in a weakly-supervised manner. Within
this pipeline, the class activation map (CAM) is obtained and further processed
to serve as a pseudo label to train the semantic segmentation model in a
fully-supervised manner. In this paper, we argue that the lost structure
information in CAM limits its application in downstream semantic segmentation,
leading to deteriorated predictions. Furthermore, the inconsistent class
activation scores inside the same object contradicts the common sense that each
region of the same object should belong to the same semantic category. To
produce sharp prediction with structure information, we introduce an auxiliary
semantic boundary detection module, which penalizes the deteriorated
predictions. Furthermore, we adopt smoothness loss to encourage prediction
inside the object to be consistent. Experimental results on the PASCAL-VOC
dataset illustrate the effectiveness of the proposed solution.
