Technology of Graphic & Image
|
1912-1920

Visual feature contrast decoupling for generalized zero-shot learning

Zhang Zhiyuan1,2
Yang Guan1,2
Liu Xiaoming1,2
Liu Yang3
1. Zhongyuan University of Technology, Zhengzhou 450007, China
2. Henan Key Laboratory on Public Opinion Intelligent Analysis, Zhengzhou 450007, China
3. Xidian University, Xi'an 710071, China

Abstract

Generalized zero-shot learning usually uses the deep model pre-trained on ImageNet to extract corresponding visual features. However, visual features extracted by the pre-trained model inevitably contain semantically irrelevant information, which will lead to the deviation of semantic-visual alignment and negative transfer to unseen classes, thus affecting the classification results. To solve the above problems, this paper proposed a generalized zero-shot learning model for visual feature contrast decoupling, which reduced the impact of redundant information on classification results by decoupling out the semanticrelated representation of visual features. Specifically, conditional variational auto-encoder firstly generated the visual features of unseen classes. Then decoupling module decoupled them into semantic-related and semantic-unrelated latent representations. Meanwhile, it applied total correlation penalty and contrastive loss to encourage the mutual independence of the two representations, and used semantic relationship matching model to measure its semantic consistency and thus guiding the model to learn semantic-related representations. Finally, it used features refined by feature refinement module and semantic-related representations to jointly learn a GZSL classifier. The experiments on all four data sets obtain superior results, confirming the effectiveness of the proposed method.

Foundation Support

国家自然科学基金青年项目(61906141)
东北师范大学应用统计教育部重点实验室资助项目(135131007)
河南省高等学校重点科研项目(23A520022)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.10.0534
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 6
Section: Technology of Graphic & Image
Pages: 1912-1920
Serial Number: 1001-3695(2023)06-050-1912-09

Publish History

[2023-01-04] Accepted Paper
[2023-06-05] Printed Article

Cite This Article

张志远, 杨关, 刘小明, 等. 视觉特征对比解耦的广义零样本学习 [J]. 计算机应用研究, 2023, 40 (6): 1912-1920. (Zhang Zhiyuan, Yang Guan, Liu Xiaoming, et al. Visual feature contrast decoupling for generalized zero-shot learning [J]. Application Research of Computers, 2023, 40 (6): 1912-1920. )

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.

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