Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 10
3196-3200

Adversarial learning method for weakly supervised semantic segmentation

Luo Huilan
Chen Hu
School of Information Engineering, Jiangxi University of Science & Technology, Ganzhou Jiangxi 341000, China
DOI: 10.19734/j.issn.1001-3695.2020.11.0433

Abstract

Most of the solutions for weakly supervised semantic segmentation use image level supervised information to generate a class-activated feature map for training and learning. The class-activated feature map can only discover the most discriminative part of the target, which has a large gap with the real pixel-level label information, so the training effect is not satisfactory. This paper proposed an adversarial learning process on the class-activated feature map from the original image and its affine variation in order to achieve a better training effect. Firstly, it input the image and the affine image into the Siamese network to obtain their respective class-activated feature maps using the image-level classification labels, and then input the two sets of class-activated feature maps into the discrimination network for adversarial learning. The gap between feature maps and real pixel-level labels improved the performance of weak supervision. On the PASACAL VOC 2012 dataset, it achieves 63.7% mIoU scores on the validation set and 65.7% mIoU scores on the test set. Comparing with other current state-of-the-art weakly supervised semantic segmentation methods, the level-crossing ratio on the validation and test sets are improved by 1.2% and 1.3%. The proposed adversarial learning scheme can effectively utilize the equal-variable attention mechanism to learn more effective information and narrow the gap between the class-activated feature maps and the real pixel-level labels, improving the performance of weak supervision to achieve a good segmentation effect.

Foundation Support

国家自然科学基金资助项目(61462035,61862031)
江西省赣州市科技创新人才计划资助项目
江西省学位与研究生教育教学改革研究重点项目(JXYJG-2020-120)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.11.0433
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 10
Section: Technology of Graphic & Image
Pages: 3196-3200
Serial Number: 1001-3695(2021)10-056-3196-05

Publish History

[2021-10-05] Printed Article

Cite This Article

罗会兰, 陈虎. 弱监督语义分割的对抗学习方法 [J]. 计算机应用研究, 2021, 38 (10): 3196-3200. (Luo Huilan, Chen Hu. Adversarial learning method for weakly supervised semantic segmentation [J]. Application Research of Computers, 2021, 38 (10): 3196-3200. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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