[1]LYU Fu,ZHANG Xu,ZHANG Ziyang.Multi-scale feature refinement for few-shot image classification[J].CAAI Transactions on Intelligent Systems,2026,21(3):675-687.[doi:10.11992/tis.202505026]
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Multi-scale feature refinement for few-shot image classification

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 3 Page number: 675-687 Column: 学术论文—机器学习 Public date: 2026-05-05
Title:
Multi-scale feature refinement for few-shot image classification
Author(s):
LYU Fu1; 2;  ZHANG Xu1;  ZHANG Ziyang1
1. School of Software, Liaoning Technical University, Huludao 125105, China;
2. Department of Basic Teaching, Liaoning Technical University, Huludao 125105, China
Keywords:
few-shot learning; metric learning; feature fusion; feature refinement; image classification; prototypical networks; multi-scale feature; deep learning
CLC:
TP391.4
DOI:
10.11992/tis.202505026
Abstract:
In few-shot image classification tasks, feature fusion improves the utilization of limited samples but can readily introduce redundant information and degrade the model’s discriminative ability. To address this, this paper proposes a multi-scale feature fusion refinement network (MFRNet) that optimizes feature representation through a systematic redundancy suppression mechanism. First, a dual-stream attention fusion module is constructed, combining cross-level feature interaction with adaptive global attention to achieve collaborative fusion across spatial and channel dimensions and effectively suppress initial redundancy. Second, a separable channel attention mechanism is introduced, using deep separable convolutions to decouple spatial filtering from channel interaction, further compressing redundant information and improving feature discriminability. Finally, an adaptive feature refinement network is designed to achieve hierarchical filtering of redundancy and retention of discriminative features through a two-stage compression–release mechanism and heterogeneous feature decomposition. Experiments on five datasets of varying granularity show that MFRNet outperforms existing mainstream methods in both experimental settings, demonstrating significant performance advantages.
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