  Recently, several weakly supervised learning methods have been devoted to
utilize bounding box supervision for training deep semantic segmentation
models. Most existing methods usually leverage the generic proposal generators
(\eg, dense CRF and MCG) to produce enhanced segmentation masks for further
training segmentation models. These proposal generators, however, are generic
and not specifically designed for box-supervised semantic segmentation, thereby
leaving some leeway for improving segmentation performance. In this paper, we
aim at seeking for a more accurate learning-based class-agnostic pseudo mask
generator tailored to box-supervised semantic segmentation. To this end, we
resort to a pixel-level annotated auxiliary dataset where the class labels are
non-overlapped with those of the box-annotated dataset. For learning pseudo
mask generator from the auxiliary dataset, we present a bi-level optimization
formulation. In particular, the lower subproblem is used to learn
box-supervised semantic segmentation, while the upper subproblem is used to
learn an optimal class-agnostic pseudo mask generator. The learned pseudo
segmentation mask generator can then be deployed to the box-annotated dataset
for improving weakly supervised semantic segmentation. Experiments on PASCAL
VOC 2012 dataset show that the learned pseudo mask generator is effective in
boosting segmentation performance, and our method can further close the
performance gap between box-supervised and fully-supervised models. Our code
will be made publicly available at
https://github.com/Vious/LPG_BBox_Segmentation .
