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
this https URL

为了解决弱监督语义分割中伪掩模生成和嘈杂伪掩模监督训练的问题，我们提出了一种新的方法，利用系数变化平滑、比例伪掩模生成、假冒欠拟合和循环伪掩模等技术，取得了 PAS-CAL VOC 2012 和 MS COCO 2014 两个语义分割数据集的最新成果，在性能方面全面超越之前的结果。