[1]张冀,曹艺,王亚茹,等.融合VAE和StackGAN的零样本图像分类方法[J].智能系统学报,2022,17(3):593-601.[doi:10.11992/tis.202107012]
 ZHANG Ji,CAO Yi,WANG Yaru,et al.Zero-shot image classification method combining VAE and StackGAN[J].CAAI Transactions on Intelligent Systems,2022,17(3):593-601.[doi:10.11992/tis.202107012]
点击复制

融合VAE和StackGAN的零样本图像分类方法

参考文献/References:
[1] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60: 84–90.
[2] LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436–444.
[3] BIEDERMAN I. Recognition-by-components: a theory of human image understanding[J]. Psychological review, 1987, 94(2): 115–147.
[4] PALATUCCI M, POMERLEAU D, HINTON G E, et al. Zero-shot Learning with semantic output codes[C]// Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Vancouver: NIPS, 2009: 1410–1418.
[5] LAMPERT C H, NICKISCH H, HARMELING S. Learning to detect unseen object classes by between-class attribute transfer[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami: IEEE, 2009: 951–958.
[6] ROHRBACH M, STARK M, SCHIELE B. Evaluating knowledge transfer and zero-shot learning in a large-scale setting[C]// Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. IEEE, 2011: 1641–1648.
[7] HABIBIAN A, MENSINK T, SNOEK C G M. Video2vec embeddings recognize events when examples are scarce[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(10): 2089–2103.
[8] 冀中, 汪浩然, 于云龙, 等. 零样本图像分类综述: 十年进展[J]. 中国科学:信息科学, 2019, 49(10): 1299–1320
JI Zhong, WANG Haoran, YU Yunlong, et al. A decadal survey of zero-shot image classification[J]. Scientia sinica (informationis), 2019, 49(10): 1299–1320
[9] 张鲁宁, 左信, 刘建伟. 零样本学习研究进展[J]. 自动化学报, 2020, 46(1): 1–23
ZHANG Luning, ZUO Xin, LIU Jianwei. Research and development on zero-shot learning[J]. Acta automatica sinica, 2020, 46(1): 1–23
[10] 冀中, 孙涛, 于云龙. 一种基于直推判别字典学习的零样本分类方法[J]. 软件学报, 2017, 28(11): 2961–2970
JI Zhong, SUN Tao, YU Yunlong. Transductive discriminative dictionary learning approach for zero-shot classification[J]. Journal of software, 2017, 28(11): 2961–2970
[11] WANG Xuesong, CHEN Chen, CHENG Yuhu. Zero-shot learning by exploiting class-related and attribute-related prior knowledge[J]. IET computer vision, 2016, 10(6): 483–492.
[12] 赵鹏, 汪纯燕, 张思颖, 等. 一种基于融合重构的子空间学习的零样本图像分类方法[J]. 计算机学报, 2021, 44(2): 409–421
ZHAO Peng, WANG Chunyan, ZHANG Siying, et al. A zero-shot image classification method based on subspace learning with the fusion of reconstruction[J]. Chinese journal of computers, 2021, 44(2): 409–421
[13] DU Yujiao, XIAO Bo, XU Wenchao, et al. Destination prediction for sharing-bikes’ trips[C]//2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC). Guiyang: IEEE, 2018: 198–202.
[14] 陈祥凤, 陈雯柏. 度量学习改进语义自编码零样本分类算法[J]. 北京邮电大学学报, 2018, 41(4): 69–75
CHEN Xiangfeng, CHEN Wenbai. Improving semantic autoencoder zero-shot classification algorithm by metric learning[J]. Journal of Beijing university of posts and telecommunications, 2018, 41(4): 69–75
[15] 吴晨, 袁昱纬, 王宏伟, 等. 基于词向量融合的遥感场景零样本分类算法[J]. 计算机科学, 2019, 46(12): 286–291
WU Chen, YUAN Yuwei, WANG Hongwei, et al. Word vectors fusion based remote sensing scenes zero-shot classification algorithm[J]. Computer science, 2019, 46(12): 286–291
[16] 冀中, 谢于中, 庞彦伟. 基于典型相关分析和距离度量学习的零样本学习[J]. 天津大学学报(自然科学与工程技术版), 2017, 50(8): 813–820
JI Zhong, XIE Yuzhong, PANG Yanwei. Zero-shot learning based on canonical correlation analysis and distance metric learning[J]. Journal of Tianjin University (science and technology edition), 2017, 50(8): 813–820
[17] XIAN Yongqin, LAMPERT C H, SCHIELE B, et al. Zero-shot learning-A comprehensive evaluation of the good, the bad and the ugly[J]. IEEE transactions on pattern analysis and machine intelligence, 2019, 41(9): 2251–2265.
[18] XIAN Yongqin, LORENZ T, SCHIELE B, et al. Feature generating networks for zero-shot learning[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 5542–5551.
[19] SARIYILDIZ M B, CINBIS R G. Gradient matching generative networks for zero-shot learning[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach: IEEE, 2019: 2163–2173.
[20] MANDAL D, NARAYAN S, DWIVEDI S K, et al. Out-of-distribution detection for generalized zero-shot action recognition[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach : IEEE, 2019: 9977–9985.
[21] XIAN Yongqin, SHARMA S, SCHIELE B, et al. F-VAEGAN-D2: a feature generating framework for any-shot learning[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach : IEEE, 2019: 10267–10276.
[22] KIM H, LEE J, BYUN H. Unseen image generating domain-free networks for generalized zero-shot learning[J]. Neurocomputing, 2020, 411: 67–77.
[23] VERMA V K, BRAHMA D, RAI P. Meta-learning for generalized zero-shot learning[J]. Proceedings of the AAAI conference on artificial intelligence, 2020, 34(4): 6062–6069.
[24] MA Yuanbo, XU Xing, SHEN Fumin, et al. Similarity preserving feature generating networks for zero-shot learning[J]. Neurocomputing, 2020, 406: 333–342.
[25] LIU Huan, YAO Lina, ZHENG Qinghua, et al. Dual-stream generative adversarial networks for distributionally robust zero-shot learning[J]. Information sciences, 2020, 519: 407–422.
[26] LIU Jinlu, ZHANG Zhaocheng, YANG Gang. Cross-class generative network for zero-shot learning[J]. Information sciences, 2021, 555: 147–163.
[27] TANG C, HE Z, LI Y, ET Al. Zero-shot learning via structure-aligned generative adversarial network[J]. IEEE transactions on neural networks and learning systems, 2021(99): 1–14.
[28] LI Zhiqun, CHEN Qiong, LIU Qingfa. Augmented semantic feature based generative network for generalized zero-shot learning[J]. Neural networks:the official journal of the international neural network society, 2021, 143: 1–11.
[29] GAO RUI, HOU XINGSONG, QIN JIE, et al. Zero-VAE-GAN: generating unseen features for generalized and transductive zero-shot learning[J]. IEEE transactions on image processing, 2020, 29: 3665–3680.
[30] KINGMA D P, WELLING M. Auto-Encoding Variational Bayes[EB/OL]. (2014–05–01)[2022–03–10]https://arxiv.org/abs/1312.6114v2.
[31] LARSEN A B L, S?NDERBY S K, LAROCHELLE H, et al. Autoencoding beyond pixels using a learned similarity metric[EB/OL]. (2016–02–10)[2022–03–10]https://arxiv.org/abs/1512.09300.
[32] ZHANG Han, XU Tao, LI Hongsheng, et al. StackGAN: text to photo-realistic image synthesis with stacked generative adversarial networks[EB/OL]. (2017–08–05)[2022-03-10]https://arxiv.org/abs/1612.03242v2.
[33] Wah C, Branson S, Welinder P, et al. The Caltech-UCSD Birds-200-2011 Dataset[J]. California institute of technology, 2011.
[34] LAMPERT C H, NICKISCH H, HARMELING S. Attribute-based classification for zero-shot visual object categorization[J]. IEEE transactions on pattern analysis and machine intelligence, 2014, 36(3): 453–465.
[35] CHE TONG, LI YANRAN, JACOB A P, et al. Mode regularized generative adversarial networks[EB/OL]. (2017–03–02)[2022–03–10]https://arxiv.org/abs/1612.02136 .
[36] 魏宏喜, 张越. 基于生成对抗网络的零样本图像分类[J]. 北京航空航天大学学报, 2019, 45(12): 2345–2350
WEI Hongxi, ZHANG Yue. Zero-shot image classification based on generative adversarial network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(12): 2345–2350
相似文献/References:
[1]张媛媛,霍静,杨婉琪,等.深度信念网络的二代身份证异构人脸核实算法[J].智能系统学报,2015,10(2):193.[doi:10.3969/j.issn.1673-4785.201405060]
 ZHANG Yuanyuan,HUO Jing,YANG Wanqi,et al.A deep belief network-based heterogeneous face verification method for the second-generation identity card[J].CAAI Transactions on Intelligent Systems,2015,10():193.[doi:10.3969/j.issn.1673-4785.201405060]
[2]丁科,谭营.GPU通用计算及其在计算智能领域的应用[J].智能系统学报,2015,10(1):1.[doi:10.3969/j.issn.1673-4785.201403072]
 DING Ke,TAN Ying.A review on general purpose computing on GPUs and its applications in computational intelligence[J].CAAI Transactions on Intelligent Systems,2015,10():1.[doi:10.3969/j.issn.1673-4785.201403072]
[3]马晓,张番栋,封举富.基于深度学习特征的稀疏表示的人脸识别方法[J].智能系统学报,2016,11(3):279.[doi:10.11992/tis.201603026]
 MA Xiao,ZHANG Fandong,FENG Jufu.Sparse representation via deep learning features based face recognition method[J].CAAI Transactions on Intelligent Systems,2016,11():279.[doi:10.11992/tis.201603026]
[4]刘帅师,程曦,郭文燕,等.深度学习方法研究新进展[J].智能系统学报,2016,11(5):567.[doi:10.11992/tis.201511028]
 LIU Shuaishi,CHENG Xi,GUO Wenyan,et al.Progress report on new research in deep learning[J].CAAI Transactions on Intelligent Systems,2016,11():567.[doi:10.11992/tis.201511028]
[5]马世龙,乌尼日其其格,李小平.大数据与深度学习综述[J].智能系统学报,2016,11(6):728.[doi:10.11992/tis.201611021]
 MA Shilong,WUNIRI Qiqige,LI Xiaoping.Deep learning with big data: state of the art and development[J].CAAI Transactions on Intelligent Systems,2016,11():728.[doi:10.11992/tis.201611021]
[6]王亚杰,邱虹坤,吴燕燕,等.计算机博弈的研究与发展[J].智能系统学报,2016,11(6):788.[doi:10.11992/tis.201609006]
 WANG Yajie,QIU Hongkun,WU Yanyan,et al.Research and development of computer games[J].CAAI Transactions on Intelligent Systems,2016,11():788.[doi:10.11992/tis.201609006]
[7]黄心汉.A3I:21世纪科技之光[J].智能系统学报,2016,11(6):835.[doi:10.11992/tis.201605022]
 HUANG Xinhan.A3I: the star of science and technology for the 21st century[J].CAAI Transactions on Intelligent Systems,2016,11():835.[doi:10.11992/tis.201605022]
[8]宋婉茹,赵晴晴,陈昌红,等.行人重识别研究综述[J].智能系统学报,2017,12(6):770.[doi:10.11992/tis.201706084]
 SONG Wanru,ZHAO Qingqing,CHEN Changhong,et al.Survey on pedestrian re-identification research[J].CAAI Transactions on Intelligent Systems,2017,12():770.[doi:10.11992/tis.201706084]
[9]杨梦铎,栾咏红,刘文军,等.基于自编码器的特征迁移算法[J].智能系统学报,2017,12(6):894.[doi:10.11992/tis.201706037]
 YANG Mengduo,LUAN Yonghong,LIU Wenjun,et al.Feature transfer algorithm based on an auto-encoder[J].CAAI Transactions on Intelligent Systems,2017,12():894.[doi:10.11992/tis.201706037]
[10]王科俊,赵彦东,邢向磊.深度学习在无人驾驶汽车领域应用的研究进展[J].智能系统学报,2018,13(1):55.[doi:10.11992/tis.201609029]
 WANG Kejun,ZHAO Yandong,XING Xianglei.Deep learning in driverless vehicles[J].CAAI Transactions on Intelligent Systems,2018,13():55.[doi:10.11992/tis.201609029]

备注/Memo

收稿日期:2021-07-07。
基金项目:国家自然科学基金面上项目(61773160);河北省自然科学基金青年科学基金项目(F2021502008);中央高校基本科研业务费专项资金面上项目(2021MS081)
作者简介:张冀,副教授,博士,主要研究方向为计算机测控、故障诊断、信息融合、图像处理、深度学习。出版规划教材2部。发表学术论文20余篇;曹艺,硕士研究生,主要研究方向为计算机视觉。;王亚茹,讲师,博士,主要研究方向为模式识别与计算机视觉、数据挖掘、电力视觉。主持河北省自然科学基金青年基金项目1项,参与国家自然科学基金面上项目2项、横向科研项目多项。发表学术论文10余篇
通讯作者:王亚茹.E-mail:wangyaru@ncepu.edu.cn

更新日期/Last Update: 1900-01-01
Copyright © 《 智能系统学报》 编辑部
地址:(150001)黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话:0451- 82534001、82518134 邮箱:tis@vip.sina.com