Papers › Pseudo-mask Matters in Weakly-supervised Semantic Segmentation

Pseudo-mask Matters in Weakly-supervised Semantic Segmentation

30 Aug 2021ICCV 2021 10arXiv:2108.12995archive 2025-07-28

Yi Li, Zhanghui Kuang, Liyang Liu, Yimin Chen, Wayne Zhang

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

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Tasks

SegmentationSemantic SegmentationWeakly supervised Semantic SegmentationWeakly-Supervised Semantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Weakly-Supervised Semantic Segmentation COCO 2014 val PMM(ScaleNet101, no saliency, no RW) mIoU 40.2 #28 of 39 Archive leaderboard report
Weakly-Supervised Semantic Segmentation COCO 2014 val PMM(ResNet38, no saliency, no RW) mIoU 36.7 #30 of 39 Archive leaderboard report
Weakly-Supervised Semantic Segmentation PASCAL VOC 2012 test PMM(Res2Net101, no saliency, no RW) Mean IoU 70.5 #41 of 60 Archive leaderboard report
Weakly-Supervised Semantic Segmentation PASCAL VOC 2012 test PMM(ResNet38, no saliency, no RW) Mean IoU 69.0 #50 of 60 Archive leaderboard report
Weakly-Supervised Semantic Segmentation PASCAL VOC 2012 val PMM(Res2Net101, no saliency, no RW) Mean IoU 70.0 #45 of 73 Archive leaderboard report
Weakly-Supervised Semantic Segmentation PASCAL VOC 2012 val PMM(ResNet38) Mean IoU 68.5 #54 of 73 Archive leaderboard report
Weakly-Supervised Semantic Segmentation PASCAL VOC 2012 val PMM(ResNet38, no saliency, no RW) Mean IoU 68.5 #55 of 73 Archive leaderboard report

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