App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 7

Few-shot classification model incorporating multi-granular attention features

Han Yanqia
Gou Guangleib
Li Xiaofeia
Zhu Donghuaa
a. College of Computer Science & Engineering, b. Big Data & Artificial Intelligence lab, Chongqing University of Technology, Chongqing 400054, China
DOI: 10.19734/j.issn.1001-3695.2023.09.0513

Abstract

In the few-shot classification tasks, existing CNN models suffer from insufficient feature extraction, limited feature diversity and weak differentiation between classes in few-shot datasets, leading to low classification accuracy. To address these issues, this paper proposed a few-shot classification model called Fusion Multi-Granular Attention Feature (FMAF) . Firstly, this method incorporated multi-granularity thought into the architecture of CNN feature extraction network to enhance feature diversity. Secondly, after the multi-granular feature extraction network, FMAF added a self-attention layer to extract key features from the multi-granular image features, based on the multi-granular attention features, FMAF employed a feature fusion method to combine the information from multiple-granularity attention features, highlighted the crucial features and improved feature representativeness. Finally, this paper utilizes two classical few-shot datasets for experimental verification on miniImageNet and tieredImageNet. Experimental results show that FMAF method can effectively improve the accuracy and efficiency of classification.

Foundation Support

国家自然科学基金资助项目(62141201)
重庆市教委科学技术研究项目(202201102)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.09.0513
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 7

Publish History

[2023-12-29] Accepted Paper

Cite This Article

韩岩奇, 苟光磊, 李小菲, 等. 融合多粒度注意力特征的小样本分类模型 [J]. 计算机应用研究, 2024, 41 (7). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.09.0513. (Han Yanqi, Gou Guanglei, Li Xiaofei, et al. Few-shot classification model incorporating multi-granular attention features [J]. Application Research of Computers, 2024, 41 (7). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.09.0513. )

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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