全局依赖引导的特征重构图像分类网络

袁姮; 武景瑞; 张晟翀

doi:10.12263/DZXB.20250619

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全局依赖引导的特征重构图像分类网络

学术论文 | 更新时间：2026-06-04

- 全局依赖引导的特征重构图像分类网络
- Global Dependency Guided Feature Reconstruction for Image Classification
- 电子学报 2026年54卷第1期页码：291-307
- 作者机构：
  
  1.辽宁工程技术大学软件学院，辽宁葫芦岛 125105
  2.光电信息控制和安全技术重点实验室，天津 300308
- 作者简介：
  
  [ "袁姮女，1988年2月出生于湖北省黄冈市。现为辽宁工程技术大学软件学院副教授、硕士生导师。主要研究方向为图像与视觉信息计算、模式识别与人工智能。E-mail: lntuyuanheng@163.com" ]
  [ "武景瑞女，2000年7月出生于山西省吕梁市。现为辽宁工程技术大学软件学院硕士研究生。主要研究方向为图像与视觉信息计算、模式识别与人工智能。E-mail: wujingrui0810@163.com" ]
  [ "张晟翀男，1973年4月出生于安徽省合肥市。现为高级工程师。主要研究方向为数字信号处理、模式识别与人工智能。E-mail: zsc417@126.com" ]
- 基金信息：
  
  国家自然科学基金(61601213);辽宁省自然科学基金(20170540426);辽宁省教育厅重点基金(LJYL049)
- DOI：10.12263/DZXB.20250619
  中图分类号： TP391;
- 收稿：2025-07-13，
  
  录用：2026-01-04，
  
  纸质出版：2026-01-25
- 稿件说明：
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袁姮, 武景瑞, 张晟翀. 全局依赖引导的特征重构图像分类网络[J]. 电子学报, 2026, 54(01): 291-307.

YUAN Heng, WU Jingrui, ZHANG Shengchong. Global Dependency Guided Feature Reconstruction for Image Classification[J]. Acta Electronica Sinica, 2026, 54(01): 291-307.
袁姮, 武景瑞, 张晟翀. 全局依赖引导的特征重构图像分类网络[J]. 电子学报, 2026, 54(01): 291-307. DOI：10.12263/DZXB.20250619

YUAN Heng, WU Jingrui, ZHANG Shengchong. Global Dependency Guided Feature Reconstruction for Image Classification[J]. Acta Electronica Sinica, 2026, 54(01): 291-307. DOI：10.12263/DZXB.20250619

摘要

针对卷积神经网络在图像分类任务中存在的长距离依赖关系建模不足的问题，本文提出全局依赖引导的特征重构图像分类网络（Global Dependency guided Feature Reconstruction for image classification，GDFRNet）。GDFRNet通过设计新颖的特征重构模块（Feature Reconstruction Module，FRM）和特征优化分支，构建了一个协同工作的双路径架构，实现了长距离依赖建模和细节特征增强。FRM通过引入并行的水平与垂直方向全局平均池化，分别在两个空间维度上压缩特征并获取具有全局视野的上下文向量，将其重新映射到二维特征空间，从而建立起跨越图像全域的长距离特征依赖关系，同时结合转置卷积等操作实现对特征空间的重构，抑制无关背景噪声，强化目标主体的连贯语义表达。特征优化分支通过设计细粒度特征捕捉模块（Fine-Grained feature Capture Module，FGCM）和特征优化模块（Feature Optimization Module，FOM）提炼并融合细节信息，减少网络抽象过程中细节信息的损失。FGCM通过引入高斯-拉普拉斯卷积，捕获图像中易于丢失的细节信息；FOM负责在高分辨率特征池中对FRM处理后的全局语义特征图与FGCM提取的丰富细节特征进行自适应融合与优化。两条路径形成了“全局轮廓-局部细节”互补的工作机制，FRM提供的全局语义特征图为细节增强提供指导，确保细节强化不偏离整体语义。同时，特征优化分支所提炼的丰富底层细节又为FRM的特征重构过程提供了必要的细微信息反馈，形成了良性的优化循环。这种互补机制使网络最终融合了经过重构的语义信息与经过增强的局部细节信息，生成判别性更强的图像表征，整体上强化了模型对图像整体结构的理解能力，并提升了特征空间的判别性。论文所提模型在CIFAR-10、CIFAR-100、SVHN、Imagenette和Imagewoof五个数据集上与当前最先进模型（State-Of-The-Art，SOTA）进行了对比实验，GDFRNet均表现出了卓越的性能，与其他先进方法相比，GDFRNet在以上五个数据集上的分类精度依次平均提升了2.39%、3.73%、2.35%、3.33%和2.92%，证明了GDFRNet的有效性和先进性。

Abstract

To address the inadequacy of modelling long-range dependencies in convolutional neural networks for image classification tasks

this paper proposes the global dependency guided feature reconstruction for image classification(GDFRNet). GDFRNet constructs a synergistic dual-path architecture through the design of a novel feature reconstruction module (FRM) and a feature optimization branch

achieving both long-range dependency modelling and fine-grained feature enhancement. The FRM introduces parallel horizontal and vertical global mean pooling to compress features across two spatial dimensions. This extracts context vectors with global vision

remapping them into a two-dimensional feature space to establish long-range feature dependencies spanning the entire image. Concurrently

operations such as transposed convolutions reconstruct the feature space

suppressing irrelevant background noise while reinforcing coherent semantic representations of the target subject. The feature optimization branch refines and fuses detail information through the fine-grained feature capture module (FGCM) and feature optimization module (FOM)

reducing the loss of detail information during the network abstraction process. FGCM employs Gaussian-Laplacian convolution to focus on capturing easily lost fine details within images. The FOM performs adaptive fusion and optimization of the global semantic feature map provided by the FRM and the rich detail features extracted by the FGCM within a high-resolution feature pool. These two pathways establish a complementary “global contour-local detail” working mechanism

the global semantic map provided by the FRM guides detail enhancement

ensuring that detail reinforcement does not deviate from the overall semantic context; simultaneously

the rich underlying details refined by the feature optimization branch provide essential fine-grained feedback and task-relevant guidance for FRM’s feature reconstruction process

establishing a virtuous optimization cycle. This complementary mechanism enables the network to ultimately fuse reconstructed semantic information with enhanced local detail

generating more discriminative image representations. This holistically strengthens the model’s understanding of overall image structure and significantly enhances the discriminative power of the feature space. Comparative experiments between the proposed model and state-of-the-art (SOTA) models were conducted across five benchmark datasets: CIFAR-10

CIFAR-100

SVHN

Imagenette and Imagewoof. GDFRNet demonstrated outstanding performance across all datasets. Compared with other advanced methods

GDFRNet achieved average improvements in classification accuracy of 2.39%

3.73%

2.35%

3.33%

and 2.92% on the five datasets mentioned above

demonstrating the effectiveness and advancement of GDFRNet.

关键词

Keywords

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