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Treffer: Few-shot image classification based on class-irrelevant feature decoupling graph neural network.

Title:
Few-shot image classification based on class-irrelevant feature decoupling graph neural network.
Authors:
Li J; Engineering Research Center, Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao, China; Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, China; Key Lab of Intelligent Rehabilitation and Neuroregulation in Hebei Province, Yanshan University, Qinhuangdao Hebei Province, 066004, China. Electronic address: leejq@stumail.ysu.edu.cn., Wen S; Engineering Research Center, Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao, China; Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, China; Key Lab of Intelligent Rehabilitation and Neuroregulation in Hebei Province, Yanshan University, Qinhuangdao Hebei Province, 066004, China. Electronic address: swen@ysu.edu.cn., Manfredi L; Department of Institute for Medical Science and Technology(IMSaT), University of Dundee, United Kingdom. Electronic address: l.manfredi@dundee.ac.uk., Karimi HR; Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy. Electronic address: hamidreza.karimi@polimi.it.
Source:
Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2026 Mar; Vol. 195, pp. 108270. Date of Electronic Publication: 2025 Oct 30.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE
Imprint Name(s):
Original Publication: New York : Pergamon Press, [c1988-
MeSH Terms:
Image Processing, Computer-Assisted*/methods , Pattern Recognition, Automated*/methods , Neural Networks, Computer*, Humans ; Algorithms ; Attention ; Graph Neural Networks
Contributed Indexing:
Keywords: Class-irrelevant feature decoupling; Few-shot image classification; Graph neural network; Measurement
Entry Date(s):
Date Created: 20251109 Date Completed: 20260124 Latest Revision: 20260128
Update Code:
20260130
DOI:
10.1016/j.neunet.2025.108270
PMID:
41207082
Database:
MEDLINE

Weitere Informationen

In the field of computer vision, similarity measurement serves as a pivotal metric for assessing the similarity or dissimilarity of image features. Image classification models utilize the results of these measurements to accomplish classification tasks. However, the inherent limitations of these measurement methods can mislead the model's judgment regarding classification objects. Additionally, the deviation of the target areas caused by the coupling phenomenon between irrelevant backgrounds and classification objects has long been neglected. In this paper, we propose a novel Class-irrelevant Feature Decoupling Graph Neural Network (CFDGNN) to address the bias problem in the model's attention resulting from the limitations of similarity measurement methods and class-irrelevant feature coupling. The proposed framework consists of three modules, including Adaptive Class Salience Channel Weighting Module (ACS), Main Object Focus Spatial Attention Module (MFS) and Feature Decoupling Graph Neural Network (FDGNN). ACS prioritizes high-discrimination salient features while suppressing other salient features that are not relevant to the task. MFS filters out irrelevant noise from the spatial dimension and optimizes the object's texture. FDGNN prevents the model's attention from being misled by simple and irrelevant information. The experimental results on miniImageNet, CIFAR-FS and CUB-200-2011 datasets show that the proposed algorithm can have more accurate classification than other algorithms, meanwhile correct the attention area of the vision model. Additionally, the visualization experiment can save the main texture and filter out irrelevant background textures.
(Copyright © 2025 Elsevier Ltd. All rights reserved.)

Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Shuhuan Wen reports financial support was provided by National Natural Science Foundation of China. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.