Image Classification Algorithm Based on Coordinate Importance Pooling and Decoupled Class Alignment Distillation

LIU Ying; XUE Jia-hao; ZHANG Wei-dong; XU Zhi-jie

doi:10.12263/DZXB.20240754

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Image Classification Algorithm Based on Coordinate Importance Pooling and Decoupled Class Alignment Distillation

PAPERS | 更新时间：2025-12-08

- Image Classification Algorithm Based on Coordinate Importance Pooling and Decoupled Class Alignment Distillation
- ACTA ELECTRONICA SINICA Vol. 53, Issue 3, Pages: 962-973(2025)
- 作者机构：
  
  1.西安邮电大学图像与信息处理研究所，陕西西安 710121
  2.无线通信与信息处理技术国际联合研究中心，陕西西安 710121
  3.英国哈德斯菲尔德大学，西约克郡 HD13DH
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62106195)
- DOI：10.12263/DZXB.20240754
  CLC： TP391;
- Received：13 August 2024，
  
  Revised：2025-01-13，
  
  Published：25 March 2025
- 稿件说明：
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刘颖, 薛家昊, 张伟东, 等. 基于坐标重要性池化和解耦类别对齐蒸馏的图像分类算法[J]. 电子学报, 2025, 53(03): 962-973.

LIU Ying, XUE Jia-hao, ZHANG Wei-dong, et al. Image Classification Algorithm Based on Coordinate Importance Pooling and Decoupled Class Alignment Distillation[J]. Acta Electronica Sinica, 2025, 53(03): 962-973.
刘颖, 薛家昊, 张伟东, 等. 基于坐标重要性池化和解耦类别对齐蒸馏的图像分类算法[J]. 电子学报, 2025, 53(03): 962-973. DOI：10.12263/DZXB.20240754

LIU Ying, XUE Jia-hao, ZHANG Wei-dong, et al. Image Classification Algorithm Based on Coordinate Importance Pooling and Decoupled Class Alignment Distillation[J]. Acta Electronica Sinica, 2025, 53(03): 962-973. DOI：10.12263/DZXB.20240754

摘要

为提高卷积神经网络图像分类精度的同时实现网络轻量化，本文提出一种基于坐标重要性池化和解耦类别对齐蒸馏的图像分类算法.首先，设计一种坐标重要性池化模块并将其嵌入ResNet34，充分利用图像像素的位置信息，以增强其判别重要性特征的能力；其次，采用BlurPool缓解在下采样过程中移位等变性丢失对网络性能的影响，以此构建教师网络；最后，构造一种解耦类别对齐蒸馏算法，分别考虑目标类和非目标类的知识并引入类别之间的关联信息，以高效地将分类知识从教师网络迁移到轻量级MobileNetV3学生网络.在不同数据集上的实验结果表明，本文提出的教师网络有效提高了分类性能，且蒸馏训练后的学生网络明显优于其他同量级网络，实现了更优越的综合性能，能够更好地应用于计算和内存资源受限的实际场景.

Abstract

An image classification algorithm based on coordinate importance pooling and decoupled class alignment distillation is proposed to improve the image classification accuracy of convolutional neural networks while achieving network lightweighting. Firstly

a coordinate importance pooling module is designed and embedded it into ResNet34

in order to fully utilize the positional information of image pixels to enhance the ability to discriminate important features. Secondly

BlurPool is used to mitigate the impact on network performance due to shift equivariance during down-sampling

and to construct the teacher network. Finally

the decoupled class alignment distillation algorithm was constructed to efficiently migrate image classification knowledge from the teacher network to the lightweight MobileNetV3 network

which considers the knowledge of target and non-target class separately and introduces correlation information between the class. The experimental results on different datasets showed that the proposed teacher network effectively improves the classification performance

and the distillation-trained student network achieves superior overall performance than other networks of the same magnitude

making it better applicable to practical scenarios with limited computational and storage power.

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Keywords

references

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