Pseudo-mask Matters in Weakly-supervised Semantic Segmentation

By Yi Li, Zhanghui Kuang, Liyang Liu, Yimin Chen, Wayne Zhang. 2021

108 citations recorded by the source corpus.

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

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

GroupViT: Semantic Segmentation Emerges from Text Supervision (2022, background): “Another line of related research is of weaklysupervised semantic segmentation [1,9,26,31,36,40,65,68, 80], which aims to learn semantic segmentation with only image-level object category supervision.”
Open-Vocabulary Image Segmentation (2021, background): “Weakly-supervised semantic segmentation can train with image-level labels [23,29,34,45,49], of which reﬁning CAMs [53] is a popular techniques.”
Scaling Open-Vocabulary Image Segmentation with Image-Level Labels (2021, background): “Semantic segmentation with less supervisions: Weakly-supervised semantic segmentation trains with image-level labels [36,47,24,31,52], of which refining CAMs [60] is a popular techniques.”