基于多分辨率自蒸馏网络的小样本图像分类

doi:10.19678/j.issn.1000-3428.0064011

计算机工程 ›› 2022, Vol. 48 ›› Issue (12): 232-240. doi: 10.19678/j.issn.1000-3428.0064011

基于多分辨率自蒸馏网络的小样本图像分类

仇真^1,2,3, 奚雪峰^1,2,3, 崔志明^1,2,3, 盛胜利⁴, 胡伏原^1,2,3

1. 苏州科技大学电子与信息工程学院, 江苏苏州 215000;
2. 苏州市虚拟现实智能交互及应用重点实验室, 江苏苏州 215000;
3. 苏州智慧城市研究院, 江苏苏州 215000;
4. 德州理工大学计算机科学系, 美国拉伯克 79401

收稿日期:2022-02-23 修回日期:2022-05-02 发布日期:2022-05-24
作者简介:仇真（1998—），男，硕士研究生，主研方向为小样本学习、图像处理；奚雪峰（通信作者），副教授、博士；崔志明，教授、博士、博士生导师；盛胜利、胡伏原，教授、博士。
基金资助:
国家自然科学基金（61876217，61876121，62176175）；江苏省“六大人才高峰”高层次人才项目（XYDXX-086）；苏州市科技计划项目（SGC2021078）。

Few-Shot Image Classification Based on Multi-Resolution Self-Distillation Network

QIU Zhen^1,2,3, XI Xuefeng^1,2,3, CUI Zhiming^1,2,3, SHENG Shengli⁴, HU Fuyuan^1,2,3

1. School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, Jiangsu 215000, China;
2. Suzhou Key Laboratory of Virtual Reality Intelligent Interaction and Application Technology, Suzhou, Jiangsu 215000, China;
3. Suzhou Smart City Research Institute, Suzhou, Jiangsu 215000, China;
4. Computer Science Department, Texas Tech University, Lubbock 79401, USA

Received:2022-02-23 Revised:2022-05-02 Published:2022-05-24

摘要/Abstract

摘要： 因图像数据具有大量的空间冗余信息，传统的多分辨率网络在处理图像数据时会产生较高的计算成本。而自蒸馏学习方法能够在精度与计算成本之间实现动态平衡，使模型在不增加网络深度和宽度的基础上，有效地提高模型精度。提出一种多分辨率自蒸馏网络（MRSDN），用于解决小样本学习中输入样本的空间冗余问题。从原始网络中分出一个浅层子网络以识别图像的低分辨率表示，并且保持该原始网络识别高分辨率图像特征的能力。同时，在多分辨率网络中引入改进的全局注意力机制，以减少信息损失且放大全局交互表示。利用自蒸馏学习方法将网络中更深层的知识压缩到浅层子网络中，以提升浅层子网络的泛化能力。在此基础上，将低分辨率网络中的粗粒度特征融合到高分辨率网络中，从而提高模型提取图像特征的能力。实验结果表明，在Mini-ImageNet数据集上MRSDN网络对5-way 1-shot与5-way 5-shot任务的准确率分别为56.34%和74.35%，在Tiered-ImageNet数据集上对5-way 1-shot与5-way 5-shot任务的准确率分别为59.56%和78.96%，能有效缓解高分辨率图像输入时的空间冗余问题，提高小样本图像分类的准确率。

关键词: 自蒸馏学习, 小样本学习, 多分辨率网络, 空间冗余, 全局注意力

Abstract: The traditional multi-resolution network incurs high computing costs when processing image data owing to a large amount of spatial redundancy information in the image data.The Self-Distillation(SD) learning method can achieve a dynamic balance between accuracy and calculation cost, effectively improving the accuracy of the model without increasing the depth and width of the network.A Multi-Resolution Self-Distillation Network(MRSDN) is proposed to solve the spatial redundancy of input samples in Few-Shot Learning(FSL).A shallow sub-network is separated from the original network to recognize the low-resolution representation of the image, and the ability of the original network to recognize the high-resolution image features is maintained.In addition, an improved Global Attention Mechanism(GAM) is introduced into the multi-resolution network to reduce information loss and enlarge the global interactive representation.The SD learning method is used to compress the in-depth knowledge of the network into a shallow sub-network to improve the generalization ability of the shallow sub-network.The coarse granularity features in the low-resolution network are fused into the high-resolution network to improve the ability of the model to extract image features.The experimental results show that accuracy of the MRSDN network for five-way one-shot and five-way five-shot tasks on the Mini-ImageNet dataset are 56.34% and 74.35%, respectively.The accuracy of the network for five-way one-shot and five-way five-shot tasks on the Tiered-ImageNet dataset are 59.56% and 78.96%, respectively.The proposed network can effectively alleviate the spatial redundancy when inputting high-resolution images, improving the accuracy of few-shot image classification.

Key words: Self-Distillation(SD), Few-Shot Learning(FSL), multi-resolution network, spatial redundancy, global attention

中图分类号:

TP391

仇真, 奚雪峰, 崔志明, 盛胜利, 胡伏原. 基于多分辨率自蒸馏网络的小样本图像分类[J]. 计算机工程, 2022, 48(12): 232-240.

QIU Zhen, XI Xuefeng, CUI Zhiming, SHENG Shengli, HU Fuyuan. Few-Shot Image Classification Based on Multi-Resolution Self-Distillation Network[J]. Computer Engineering, 2022, 48(12): 232-240.

https://www.ecice06.com/CN/Y2022/V48/I12/232

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