  Existing weakly-supervised semantic segmentation methods using image-level
annotations typically rely on initial responses to locate object regions.
However, such response maps generated by the classification network usually
focus on discriminative object parts, due to the fact that the network does not
need the entire object for optimizing the objective function. To enforce the
network to pay attention to other parts of an object, we propose a simple yet
effective approach that introduces a self-supervised task by exploiting the
sub-category information. Specifically, we perform clustering on image features
to generate pseudo sub-categories labels within each annotated parent class,
and construct a sub-category objective to assign the network to a more
challenging task. By iteratively clustering image features, the training
process does not limit itself to the most discriminative object parts, hence
improving the quality of the response maps. We conduct extensive analysis to
validate the proposed method and show that our approach performs favorably
against the state-of-the-art approaches.
