This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
W. L. He M.L. Zhu. Current status and future analysis of capsule neural network research [J] Computer Engineering and Application, 2021, 57(03):33–43.HeW. L.ZhuM.L.. Current status and future analysis of capsule neural network research [J], 2021, 57(03):33–43.Search in Google Scholar
Yang Jucheng Han Shujie Mao Lei et al. A review of capsule network modeling [J] Journal of Shandong University (Engineering Edition), 2019, 49(06):1–10.JuchengYangShujieHanLeiMaoA review of capsule network modeling [J], 2019, 49(06):1–10.Search in Google Scholar
Zheng Yuanpan Li Guangyang Li Ye. A research review of deep learning in image recognition [J]. Computer Engineering and Applications, 2019, 55(12):20–36.YuanpanZhengGuangyangLiYeLi. A research review of deep learning in image recognition [J]. , 2019, 55(12):20–36.Search in Google Scholar
YANG Xiaofeng ZHANG Laifu WANG Zhipeng et al. Cross-domain pedestrian re-identification based on capsule networks [J] Computer Engineering and Science, 2021, 43(09):1591–1599.XiaofengYangLaifuZhangZhipengWangCross-domain pedestrian re-identification based on capsule networks [J], 2021, 43(09):1591–1599.Search in Google Scholar
JIANG Hong JIA Shuaiyu YAO Hongge. Capsule network for object recognition in complex realistic scenes [J] Journal of Xi’an University of Technology 2019 39(06):712719.DOI:10.16185/j.jxatu.edu.cn.2019. 06.014.HongJiangShuaiyuJiaHonggeYao. Capsule network for object recognition in complex realistic scenes [J]201939(06):712–719.DOI:10.16185/j.jxatu.edu.cn.2019.06.014.Open DOISearch in Google Scholar
Liu Linsong Tong Minglei Wu Dongliang. SA-CapsNefSelf-attentive capsule networklJl. Computer Application Research 2021 38(10):3005-3008+3039. DOI:10.19734/j.issn.10013695.2021.03.0092.LinsongLiuMingleiTongDongliangWu. SA-CapsNefSelf-attentive capsule networklJl. 202138(10):3005–3008+3039. DOI:10.19734/j.issn.10013695.2021.03.0092.Open DOISearch in Google Scholar
Qun Zhou. Research on hyperspectral remote sensing image classification based on capsule neural network [D] Northern Nationalities University, 2021. DOI:10.27754/d.cnki.gbfmz.2021.000172. ZhouQun. Northern Nationalities University, 2021. DOI:10.27754/d.cnki.gbfmz.2021.000172. Open DOISearch in Google Scholar
Yao YQ. Research on facial expression feature extraction and recognition algorithm based on capsule network [D] Beijing Jiaotong University, 2020. DOI: 10.26944/d.cnki.gbfju.2019.000835.YaoYQ.Beijing Jiaotong University, 2020. DOI: 10.26944/d.cnki.gbfju.2019.000835.Open DOISearch in Google Scholar
Lou Yue. Research on plant recognition method based on improved capsule neural network [D]. Jilin University,2021.DOI:10.27163/d.cnki.gjlnu.2020.0001 42.YueLou. . Jilin University,2021.DOI:10.27163/d.cnki.gjlnu.2020.0001 42.Open DOISearch in Google Scholar
H.H. Zhang. Research and development of security system based on Caps-Net face recognition [D]. Ximiang University, 2021. DOI:10.27429/d.cnki.gxjdu.2020.00355.ZhangH.H.. . Ximiang University, 2021. DOI:10.27429/d.cnki.gxjdu.2020.00355.Open DOISearch in Google Scholar
Shan Chen Rencheng Sun Fengjing Shao et al. Research and improvement of dynamic routing based on capsule networks [J] Computer Engineering 2022, 48(05):208–214.DOI:10.19678/j.issn.1003428.0062928.ChenShanSunRenchengShaoFengjingResearch and improvement of dynamic routing based on capsule networks [J]2022, 48(05):208–214.DOI:10.19678/j.issn.1003428.0062928.Open DOISearch in Google Scholar
Sabour, S., Frosst, N., & Hinton, G. E. (2017). Dynamic routing between capsules. In Advances in Neural Information Processing Systems (pp.3856–3866).SabourS.FrosstN. & HintonG. E. (2017). Dynamic routing between capsules. In (pp.3856–3866).Search in Google Scholar
Zhang, Y., Yang, J., & Davis, L. S. (2018). Capsule network performance on complex data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(7), 1552–1566.ZhangY.YangJ. & DavisL. S. (2018). Capsule network performance on complex data. , 41(7), 1552–1566.Search in Google Scholar
Xiang, S., Wang, Y., Liu, Z., & Gilmore, J. H. (2019). Dynamic capsule attention for visual question answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 6255–6264).XiangS.WangY.LiuZ.GilmoreJ. H. (2019). Dynamic capsule attention for visual question answering. In (pp. 6255–6264..Search in Google Scholar
Tang, H., Yu, N., Wang, R., & Wang, M. (2019). Recurrent capsule network for person re-identification. In Proceedings of the IEEE International Conference on Computer Vision (pp. 7130–7139..TangH.YuN.WangR.WangM. (2019). Recurrent capsule network for person re-identification. (pp. 7130–7139..Search in Google Scholar
Lecun Y Bottou L et al. Gradient-based learning applied to document recognition[J]. Proceedings of IEEE, 1998, 86(11):2278–2324.LecunYBottouLGradient-based learning applied to document recognition[J]. , 1998, 86(11):2278–2324.Search in Google Scholar
Krizhevsky A. Sutskever I Hinton G E. Imagenet classification with deep convolutional neural networks [C] // Advances in neural information processing systems. 2012: 1097–1105.KrizhevskyA.SutskeverIHintonG E.Imagenet classification with deep convolutional neural networks [C] // . 2012: 1097–1105.Search in Google Scholar
Deng F Pu S Chen X et al. Hyperspectral image classification with capsule network using limited training samples [J]. Sensors, 2018, 18(9):22.DengFPuSChenXHyperspectral image classification with capsule network using limited training samples [J]. , 2018, 18(9):22.Search in Google Scholar