  Most weakly supervised semantic segmentation (WSSS) methods follow the
pipeline that generates pseudo-masks initially and trains the segmentation
model with the pseudo-masks in fully supervised manner after. However, we find
some matters related to the pseudo-masks, including high quality pseudo-masks
generation from class activation maps (CAMs), and training with noisy
pseudo-mask supervision. For these matters, we propose the following designs to
push the performance to new state-of-art: (i) Coefficient of Variation
Smoothing to smooth the CAMs adaptively; (ii) Proportional Pseudo-mask
Generation to project the expanded CAMs to pseudo-mask based on a new metric
indicating the importance of each class on each location, instead of the scores
trained from binary classifiers. (iii) Pretended Under-Fitting strategy to
suppress the influence of noise in pseudo-mask; (iv) Cyclic Pseudo-mask to
boost the pseudo-masks during training of fully supervised semantic
segmentation (FSSS). Experiments based on our methods achieve new state-of-art
results on two changeling weakly supervised semantic segmentation datasets,
pushing the mIoU to 70.0% and 40.2% on PAS-CAL VOC 2012 and MS COCO 2014
respectively. Codes including segmentation framework are released at
https://github.com/Eli-YiLi/PMM
