Branches Mutual Promotion for End-to-End Weakly Supervised Semantic
  Segmentation
Lei Zhu Hangzhou He Xinliang Zhang Qian Chen Shuang Zeng Qiushi Ren Yanye Lu
End-to-end weakly supervised semantic segmentation aims at optimizing a
segmentation model in a single-stage training process based on only image
annotations. Existing methods adopt an online-trained classification branch to
provide pseudo annotations for supervising the segmentation branch. However,
this strategy makes the classification branch dominate the whole concurrent
training process, hindering these two branches from assisting each other. In
our work, we treat these two branches equally by viewing them as diverse ways
to generate the segmentation map, and add interactions on both their
supervision and operation to achieve mutual promotion. For this purpose, a
bidirectional supervision mechanism is elaborated to force the consistency
between the outputs of these two branches. Thus, the segmentation branch can
also give feedback to the classification branch to enhance the quality of
localization seeds. Moreover, our method also designs interaction operations
between these two branches to exchange their knowledge to assist each other.
Experiments indicate our work outperforms existing end-to-end weakly supervised
segmentation methods.