Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 11
3515-3520

Refining Transformer for weakly supervised image semantic segmentation

Sun Wanchun1
Feng Xin1,2
Ma Hui3
Hu Lisong4
1. School of Computer Science & Technology, Changchun University of Science & Technology, Changchun 130022, China
2. Chongqing Research Institute, Changchun University of Science & Technology, Chongqing 401122, China
3. Computer Basic Teaching & Research Dept. , Anhui Vocational College of Police Officers, Hefei 230031, China
4. R & D Dept. , Beike Tianhui(Hefei)Laser Technology Co. , Ltd. , Hefei 230041, China
DOI: 10.19734/j.issn.1001-3695.2023.05.0218

Abstract

The weakly supervised methods for image semantic segmentation using the image-level labels are a relatively popular research direction. The class activation maps generation approach is the most commonly used approach in these researches. Due to the sparsity of class activation maps, the accuracy of discriminative regions is generally low. To address these problems, this paper proposed an improved weakly supervised image learning method based on the Transformer network. Firstly, it introduced a spatial attention interaction layer to extend the coverage of the class activation maps. Secondly, it designed an attention adaptive module to guide the model to enhance the class response in weak regions. In particular, it constructed an adpative cross domain to improve the model classification performance during class generation. The method achieves the accuracies of 73.5% and 73.0% on the Pascal VOC 2012 validation sets and test sets, respectively. The experimental results prove that the refined Transformer network learning method can improve the image semantic segmentation performance of weakly supervised method.

Foundation Support

安徽省自然科学研究重点资助项目(KJ2021A1471)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.05.0218
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 11
Section: Technology of Graphic & Image
Pages: 3515-3520
Serial Number: 1001-3695(2023)11-049-3515-06

Publish History

[2023-08-10] Accepted Paper
[2023-11-05] Printed Article

Cite This Article

孙万春, 冯欣, 马慧, 等. 细化Transformer网络的弱监督图像语义分割 [J]. 计算机应用研究, 2023, 40 (11): 3515-3520. (Sun Wanchun, Feng Xin, Ma Hui, et al. Refining Transformer for weakly supervised image semantic segmentation [J]. Application Research of Computers, 2023, 40 (11): 3515-3520. )

About the Journal

  • 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.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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