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              多階段注意力膠囊網絡的圖像分類

              宋燕 王勇

              宋燕, 王勇. 多階段注意力膠囊網絡的圖像分類. 自動化學報, 2021, 47(x): 1?14 doi: 10.16383/j.aas.c210012
              引用本文: 宋燕, 王勇. 多階段注意力膠囊網絡的圖像分類. 自動化學報, 2021, 47(x): 1?14 doi: 10.16383/j.aas.c210012
              Song Yan, Wang Yong. Multi-stage attention-based capsule networks for image classification. Acta Automatica Sinica, 2021, 47(x): 1?14 doi: 10.16383/j.aas.c210012
              Citation: Song Yan, Wang Yong. Multi-stage attention-based capsule networks for image classification. Acta Automatica Sinica, 2021, 47(x): 1?14 doi: 10.16383/j.aas.c210012

              多階段注意力膠囊網絡的圖像分類

              doi: 10.16383/j.aas.c210012
              基金項目: 國家自然科學基金 (62073223), 上海市自然科學基金 (18ZR1427100), 航天飛行動力學技術國防科技重點實驗室開放課題 (6142210200304)
              詳細信息
                作者簡介:

                宋燕:上海理工大學教授. 2001年吉林大學獲得學士學位, 2005年電子科技大學獲得碩士學位, 2013年上海交通大學獲得博士學位. 2016年至2017年, 訪問英國布魯奈爾大學主要研究方向為模式識別, 數據分析和預測控制. 本文通信作者. E-mail: sonya@usst.edu.cn

                王勇:上海理工大學碩士研究生. 2019年獲得皖西學院學士學位. 主要研究方向為圖像處理. E-mail: 18856496454@163.com

              Multi-stage Attention-Based Capsule Networks for Image Classification

              Funds: Supported in part by the National Natural Science Foundation of China under Grants (62073223), the Natural Science Foundation of Shanghai under Grant (18ZR1427100), and the Open Project of Key Laboratory of Aerospace Flight Dynamics and National Defense Science and Technology under Grants (6142210200304)
              More Information
                Author Bio:

                SONG Yan Professor at University of Shanghai for Science and Technology. She received her bachelor degree from Jilin University in 2001, the master degree from University of Electronic Science and Technology of China in 2005, and the Ph.D. degree from Shanghai Jiao Tong University in 2013. From 2016 to 2017, she visited Brunel University, UK. Her research interests include pattern recognition, data analysis and predictive control. Corresponding author of this paper

                WANG Yong Master student at University of Shanghai for Science and Technology. He received his bachelor degree from Western Anhui University in 2019. His main research interest is image processing

              • 摘要: 本文針對膠囊網絡特征提取不充分的問題, 提出了一種圖像分類的多階段注意力膠囊網絡模型. 首先在卷積層對低層特征和高層特征分別采用空間和通道注意力來提取有效特征; 然后提出基于向量方向的注意力機制作用于動態路由層, 增加對重要膠囊的關注, 進而提高低層膠囊對高層膠囊預測的準確性; 最后, 在五個公共數據集上進行對比實驗, 結果表明本文提出的模型在分類精度和魯棒性上優于其他膠囊網絡模型, 在仿射變換圖像重構上也表現良好.
              • 圖  1  膠囊網絡結構圖

                Fig.  1  The structure of CapsNet

                圖  2  多階段注意力的膠囊網絡模型

                Fig.  2  A capsule network model of multi-stage attention

                圖  3  CA和SA機制

                Fig.  3  Channel attention mechanism and spatial attention mechanism

                圖  4  向量注意力機制

                Fig.  4  Vector Attention mechanism

                圖  5  圖像重構

                Fig.  5  Image reconstruction

                圖  6  不同改進模塊在五個數據集上的迭代曲線

                Fig.  6  Iteration curves of different improvement modules over five data sets

                圖  7  原圖和仿射變換圖 (a): MNIST數據集, (b): 旋轉后的MNIST數據集

                Fig.  7  Raw image and affine image (a): MNIST dataset, (b): MNIST dataset after rotation

                圖  8  不同模型的魯棒性對比實驗

                Fig.  8  Comparison of robustness of different models

                圖  9  (a): MNIST真實圖像, (b): 膠囊網絡重構, (c): 本文模型重構

                Fig.  9  (a): Real image for MNIST, (b): capsule network reconstruction, (c): our model reconstruction

                圖  13  (a): smallNORB真實圖像, (b): 膠囊網絡重構, (c): 本文模型重構

                Fig.  13  (a): Real image for smallNORB, (b): capsule network reconstruction, (c): our model reconstruction

                圖  10  (a): Fashion-MNIST真實圖像, (b): 膠囊網絡重構, (c): 本文模型重構

                Fig.  10  (a): Real image for Fashion-MNIST, (b): capsule network reconstruction, (c): our model reconstruction

                圖  11  (a): CIFAR-10真實圖像, (b): 膠囊網絡重構, (c): 本文模型重構

                Fig.  11  (a): Real image for CIFAR-10, (b): capsule network reconstruction, (c): our model reconstruction

                圖  12  (a): SVHN真實圖像, (b): 膠囊網絡重構, (c): 本文模型重構

                Fig.  12  (a): Real image for SVHN, (b): capsule network reconstruction, (c): our model reconstruction

                圖  14  MINST數據集原圖和仿射變換圖 (a): 真實圖像, (b): 旋轉25度, (c): 旋轉?25度

                Fig.  14  Comparison of reconstruction (a): Real image, (b): 25 degrees rotation, (c): ?25 degrees rotation

                圖  15  圖14(b)的重構實驗對比圖 (a): 文獻[10]CapsNet重構, (b): 本文模型重構

                Fig.  15  Comparison of reconstruction to Fig.14(b) (a): Reconstruction by CapsNet, (b): Reconstruction by our model

                圖  16  圖14(c)的重構實驗對比圖 (a): 文獻[10]CapsNet重構, (b): 本文模型重構

                Fig.  16  Comparison of reconstruction to Fig.14(c) (a): reconstruction by CapsNet, (b): reconstruction by our model

                圖  17  本文模型和文獻[10]的CapsNet重構損失對比曲線

                Fig.  17  Comparison of reconstruction loss curves between our model and CapsNet in [10]

                表  1  不同改進模塊在五個數據集上的分類錯誤率

                Table  1  Classification error rates of different improved modules on five datasets

                模型MNISTFashion-MNISTCIFAR-10SVHNsmallNORB
                Baseline0.38%7.11%21.21%5.12%5.62%
                Baseline+(SA+CA)0.32%5.54%11.69%4.61%5.07%
                Baseline+VA0.28%5.53%14.65%4.99%5.21%
                Baseline+(SA+CA+VA)0.22%4.63%9.99%4.08%4.89%
                下載: 導出CSV

                表  2  不同模型在五個數據集上的分類錯誤率

                Table  2  Classification error rates of different models on five datasets

                模型MNISTFashion-MNISTCIFAR-10SVHNsmallNORB
                Prem Nair et al.’s CapsNet [5]0.5%10.2%31.47%8.94%
                HitNet [7]0.32%7.7%26.7%5.5%
                Matrix Capsule EM Routing [9]0.7%5.97%16.79%9.64%5.2%
                SACN [10]0.5%5.98%16.65%5.01%7.79%
                AR CapsNet [11]0.54%12.71%
                DCNet [30]0.25%5.36%17.37%4.42%5.57%
                MS-CapsNet [31]6.01%18.81%
                VB-Routing[32]5.2%11.2%4.75%1.6%
                Aff-CapsNets[33]0.46%7.47%23.72%7.85%
                Ours0.22%4.63%9.99%4.08%4.89%
                下載: 導出CSV

                表  3  不同模型的魯棒性對比實驗

                Table  3  Robustness comparison test of different models

                模型MNISTMNIST-Rotation
                CNN0.74%5.52%
                CapsNet[6]0.38%2.11%
                EM Routing[9]0.43%2.65%
                Ours0.22%0.63%
                下載: 導出CSV
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                        • 收稿日期:  2021-01-05
                        • 錄用日期:  2021-05-12
                        • 網絡出版日期:  2021-06-20

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