零样本学习综述

doi:10.3778/j.issn.1002-8331.2106-0133

摘要/Abstract

摘要：

尽管自深度学习发展以来，减少大量人工标记样本的需求使得零样本学习取得了不错的进展，以至于已经拥有比较完善的理论体系。但是对于零样本学习应用的研究寥寥无几，如何有效地对应用领域进行梳理是现阶段急需解决的问题。对零样本的理论体系进行介绍，通过一个例子引出零样本学习的定义，继而与广义零样本、监督学习比较，再而列举4个关键问题以及现有的解决方案，给出文本、图像、视频三方面常用的数据集；按照关键技术（属性、嵌入以及生成模型）出现时间顺序，对13个典型模型如何进行零样本学习展开描述，并对优点、缺点、创新点、挑选数据集以及表现进行总结；从词、图像、视频3个维度详细介绍了零样本学习在各个领域的应用；提出了零样本学习过程中出现的挑战并给出了对应的潜在研究方向。

关键词: 零样本学习, 属性, 嵌入空间, 生成模型

Abstract:

Although there have been well developed in zero-shot learning since the development of deep learning, in the aspect of the application, zero-shot learning did not have a good system to order it. This paper overviews theoretical systems of zero-shot learning, typical models, application systems, present challenges and future research directions. Firstly, it introduces the theoretical systems from definition of zero-shot learning, essential problems, and commonly used data sets. Secondly, some typical models of zero-shot learning are described in chronological order. Thirdly, it presents the application systems about of zero-shot learning from the three dimensions, such as words, images and videos. Finally, the paper analyzes the challenges and future research directions in zero-shot learning.

Key words: zero-shot learning, attribute, embedding space, general model

王泽深，杨云，向鸿鑫，柳青. 零样本学习综述[J]. 计算机工程与应用, 2021, 57(19): 1-17.

WANG Zeshen，YANG Yun，XIANG Hongxin, LIU Qing. Survey on Zero-Shot Learning[J]. Computer Engineering and Applications, 2021, 57(19): 1-17.

参考文献

[1] 王阳，王琼，陆建峰.基于语义自动编码机的零次学习研究[J].计算机与数字工程，2019，47（10）：2428-2433.
WANG Y，WANG Q，LU J F.Research on zero-shot learning based on semantic automatic coder[J].Computer and Digital Engineering，2019，47（10）：2428-2433.
[2] SUN X，GU J，SUN H.Research progress of zero-shot learning[J].Applied Intelligence，2021，51（2）：1-15.
[3] 巩萍.基于属性的零样本学习方法的研究[D].徐州：中国矿业大学，2019.
GONG P.Research on zero-shot learning methods base on attribute[D].Xuzhou：China University of Mining and Technology，2019.
[4] PALATUCCI M，POMERLEAU D，HINTON G，et al.Zero-shot learning with semantic output codes[C]//Proceedings of the 22nd International Conference on Neural Information Processing Systems，2009：1410-1418.
[5] 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 Z，WANG H R，YU Y L，et al.Review of zero-shot image classification：ten years progress[J].Chinese Science（Information Science），2019，49（10）：1299-1320.
[6] 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 L N，ZUO X，LIU J W.Research of the progress of zero-shot learning[J].Acta Automatica Sinica，2020，46（1）：1-23.
[7] 刘靖祎，史彩娟，涂冬景，等.零样本图像分类综述[J].计算机科学与探索，2021，15（5）：812-824.
LIU J Y，SHI C J，TU D J，et al.A summary of zero-shot learningin image classification[J].Journal of Frontiers of Computer Science & Technology，2021，15（5）：812-824.
[8] 兰红，方治屿.零样本图像识别[J].电子与信息学报，2020，42（5）：1188-1200.
LAN H，FANG Z Y.Zero-shot image recognition[J].Journal of Electronics & Information Technology，2020，42（5）：1188-1200.
[9] FU Y，HOSPEDALES T M，XIANG T，et al.Transductive multi-view zero-shot learning[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2015，37（11）：2332-2345.
[10] 王鑫.基于生成式模型的零样本图像分类[D].徐州：中国矿业大学，2020.
WANG X.Zero-shot image classification based on generative model[D].Xuzhou：China University of Mining and Technology，2020.
[11] 冯耀功，于剑，桑基韬，等.基于知识的零样本视觉识别综述[J].软件学报，2021，32（2）：370-405.
FENG Y G，YU J，SANG J T，et al.Review of zero-shot visual recognition base on knowledge[J].Journal of Software，2021，32（2）：370-405.
[12] YE M，GUO Y.Progressive ensemble networks for zero-shot recognition[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition（CVPR），2019：11720-11728.
[13] KODIROV E，XIANG T，FU Z，et al.Unsupervised domain adaptation for zero-shot learning[C]//Proceedings of the IEEE International Conference on Computer Vision，2015：2452-2460.
[14] ZHAO A，DING M，GUAN J，et al.Domain-invariant projection learning for zero-shot recognition[C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems，2018：1027-1038.
[15] XU X，HOSPEDALES T M，GONG S.Multi-task zero-shot action recognition with prioritised data augmentation[C]//European Conference on Computer Vision，2016：343-359.
[16] XU X，HOSPEDALES T，GONG S.Transductive zero-shot action recognition by word-vector embedding[J].arXiv：1511.04458，2015.
[17] MISHRA A，VERMA V K，REDDY M S K，et al.A generative approach to zero-shot and few-shot action recognition[C]//2018 IEEE Winter Conference on Applications of Computer Vision（WACV），2018：372-380.
[18] GAO J，ZHANG T，XU C.I know the relationships：Zero-shot action recognition via two-stream graph convolutional networks and knowledge graphs[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2019：8303-8311.
[19] FU Y，HOSPEDALES T M，XIANG T，et al.Learning multimodal latent attributes[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2014，36（2）：303-316.
[20] XU B，FU Y，JIANG Y G，et al.Video emotion recognition with transferred deep feature encodings[C]//Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval，2016：15-22.
[21] JIANG H，WANG R，SHAN S，et al.Learning class prototypes via structure alignment for zero-shot recognition[C]//Proceedings of the European Conference on Computer Vision（ECCV），2018：118-134.
[22] FU Y，HOSPEDALES T M，XIANG T，et al.Transductive multi-view embedding for zero-shot recognition and annotation[C]//Proceedings of the European Conference on Computer Vision，2014：584-599.
[23] LI Y，WANG D，HU H，et al.Zero-shot recognition using dual visual-semantic mapping paths[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：3279-3287.
[24] ZHAO B，WU B，WU T，et al.Zero-shot learning posed as a missing data problem[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops，2017：2616-2622.
[25] XU X，SHEN F，YANG Y，et al.Transductive visual-semantic embedding for zero-shot learning[C]//Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval，2017：41-49.
[26] WANG Q，CHEN K.Zero-shot visual recognition via bidirectional latent embedding[J].International Journal of Computer Vision，2017，124（3）：356-383.
[27] VERMA V K，RAI P.A simple exponential family framework for zero-shot learning[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases，2017：792-808.
[28] PU Y，VERMA V.Zero-shot learning via class-conditioned deep generative models[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2018.
[29] PAUL A，KRISHNAN N C，MUNJAL P.Semantically aligned bias reducing zero shot learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：7056-7065.
[30] XIAN Y，SHARMA S，SCHIELE B，et al.f-vaegan-d2：A feature generating framework for any-shot learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：10275-10284.
[31] GUO Y，DING G，JIN X，et al.Transductive zero-shot recognition via shared model space learning[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2016.
[32] YU Y，JI Z，GUO J，et al.Transductive zero-shot learning with adaptive structural embedding[J].IEEE Transactions on Neural Networks and Learning Systems，2017，29（9）：4116-4127.
[33] JIANG H，WANG R，SHAN S，et al.Adaptive metric learning for zero-shot recognition[J].IEEE Signal Processing Letters，2019，26（9）：1270-1274.
[34] SONG J，SHEN C，YANG Y，et al.Transductive unbiased embedding for zero-shot learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：1024-1033.
[35] HUBERT TSAI Y H，HUANG L K，SALAKHUTDINOV R.Learning robust visual-semantic embeddings[C]//Proceedings of the IEEE International Conference on Computer Vision，2017：3571-3580.
[36] CHANGPINYO S，CHAO W L，GONG B，et al.Synthesized classifiers for zero-shot learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2016：5327-5336.
[37] JAYARAMAN D，GRAUMAN K.Zero shot recognition with unreliable attributes[J].arXiv：1409.4327，2014.
[38] FU Z，XIANG T，KODIROV E，et al.Zero-shot object recognition by semantic manifold distance[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2015：2635-2644.
[39] QI G J，LIU W，AGGARWAL C，et al.Joint intermodal and intramodal label transfers for extremely rare or unseen classes[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2016，39（7）：1360-1373.
[40] FU Y，XIANG T，JIANG Y G，et al.Recent advances in zero-shot recognition：Toward data-efficient understanding of visual content[J].IEEE Signal Processing Magazine，2018，35（1）：112-125.
[41] MANDAL D，NARAYAN S，DWIVEDI S K，et al.Out-of-distribution detection for generalized zero-shot action recognition[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：9985-9993.
[42] DUTTA A，AKATA Z.Semantically tied paired cycle consistency for zero-shot sketch-based image retrieval[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：5089-5098.
[43] ZHANG C，PENG Y.Visual data synthesis via gan for zero-shot video classification[J].arXiv：1804.10073，2018.
[44] LI J，JING M，LU K，et al.Leveraging the invariant side of generative zero-shot learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：7402-7411.
[45] LIU B，DONG Q，HU Z.Zero-shot learning from adversarial feature residual to compact visual feature[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2020：11547-11554.
[46] BUCHER M，HERBIN S，JURIE F.Generating visual representations for zero-shot classification[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops，2017：2666-2673.
[47] TONG B，KLINKIGT M，CHEN J，et al.Adversarial zero-shot learning with semantic augmentation[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence，2018.
[48] XIAN Y，LORENZ T，SCHIELE B，et al.Feature generating networks for zero-shot learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：5542-5551.
[49] ZHU Y，ELHOSEINY M，LIU B，et al.A generative adversarial approach for zero-shot learning from noisy texts[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：1004-1013.
[50] NI J，ZHANG S，XIE H.Dual adversarial semantics-consistent network for generalized zero-shot learning[C]//Advances in Neural Information Processing Systems，2019：6146-6157.
[51] FELIX R，REID I，CARNEIRO G.Multi-modal cycle-consistent generalized zero-shot learning[C]//Proceedings of the European Conference on Computer Vision（ECCV），2018：21-37.
[52] HUANG H，WANG C，YU P S，et al.Generative dual adversarial network for generalized zero-shot learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：801-810.
[53] HUAN L，QINGHUA Z，MINNAN L，et al.Cross-domain adversarial learning for zero-shot classification[J].Journal of Computer Research and Development，2019，56（12）：2521.
[54] LU Z W，GUAN J H，XIANG T，et al.Zero and few shot learning with semantic feature synthesis and competitive learning[J].arXiv：1810，08332，2018.
[55] LONG Y，LIU L，SHAO L，et al.From zero-shot learning to conventional supervised classification：Unseen visual data synthesis[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：1627-1636.
[56] GUO Y，DING G，HAN J，et al.Synthesizing samples for zero-shot learning[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence，2017：1774-1780.
[57] MISHRA A，KRISHNA REDDY S，MITTAL A，et al.A generative model for zero shot learning using conditional variational autoencoders[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops，2018：2188-2196.
[58] YU H，LEE B.Zero-shot learning via simultaneous generating and learning[C]//Advances in Neural Information Processing Systems，2019：46-56.
[59] LU J，LI J，YAN Z，et al.Attribute-Based Synthetic Network（ABS-Net）：Learning more from pseudo feature representations[J].Pattern Recognition，2018，80：129-142.
[60] WANG D，LI Y，LIN Y，et al.Relational knowledge transfer for zero-shot learning[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2016.
[61] KODIROV E，XIANG T，GONG S.Semantic autoencoder for zero-shot learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：3174-3183.
[62] WAN Z，CHEN D，LI Y，et al.Transductive zero-shot learning with visual structure constraint[C]//Advances in Neural Information Processing Systems，2019：9972-9982.
[63] DINU G，LAZARIDOU A，BARONI M.Improving zero-shot learning by mitigating the hubness problem[J].arXiv：1412.6568，2014.
[64] SHIGETO Y，SUZUKI I，HARA K，et al.Ridge regression，hubness，and zero-shot learning[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases，2015：135-151.
[65] LAZARIDOU A，DINU G，BARONI M.Hubness and pollution：Delving into cross-space mapping for zero-shot learning[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing，2015：270-280.
[66] ZHANG L，XIANG T，GONG S.Learning a deep embedding model for zero-shot learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：2021-2030.
[67] CHANGPINYO S，CHAO W L，SHA F.Predicting visual exemplars of unseen classes for zero-shot learning[C]//Proceedings of the IEEE International Conference on Computer Vision，2017：3476-3485.
[68] QIN J，LIU L，SHAO L，et al.Zero-shot action recognition with error-correcting output codes[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：2833-2842.
[69] XU X，SHEN F，YANG Y，et al.Matrix tri-factorization with manifold regularizations for zero-shot learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：3798-3807.
[70] CHEN L，ZHANG H，XIAO J，et al.Zero-shot visual recognition using semantics-preserving adversarial embedding networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：1043-1052.
[71] WANG W，ZHENG V W，YU H，et al.A survey of zero-shot learning：Settings，methods，and applications[J].ACM Transactions on Intelligent Systems and Technology（TIST），2019，10（2）：1-37.
[72] SOCHER R，GANJOO M，MANNING C D，et al.Zero-shot learning through cross-modal transfer[C]//Proceedings of the 26th International Conference on Neural Information Processing Systems，2013.
[73] SHEN Y，QIN J，HUANG L，et al.Invertible zero-shot recognition flows[C]//Proceedings of European Conference on Computer Vision，2020：614-631.
[74] ZHANG Z，SALIGRAMA V.Zero-shot learning via semantic similarity embedding[C]//Proceedings of the IEEE International Conference on Computer Vision，2015：4166-4174.
[75] ZHANG Z，SALIGRAMA V.Zero-shot learning via joint latent similarity embedding[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2016：6034-6042.
[76] SPEER R，HAVASI C.ConceptNet 5：A large semantic network for relational knowledge[M]//The people’s Web meets NLP.Berlin，Heidelberg：Springer，2013：161-176.
[77] SPEER R，CHIN J，HAVASI C.ConceptNet 5.5：An open multilingual graph of general knowledge[C]//Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence，2017：4444-4451.
[78] 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，2009：951-958.
[79] WAH C，BRANSON S，WELINDER P，et al.The caltech-ucsd birds-200-2011 dataset[J].Solr，2011：1-8.
[80] FARHADI A，ENDRES I，HOIEM D，et al.Describing objects by their attributes[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition，2009：1778-1785.
[81] PATTERSON G，HAYS J.Sun attribute database：Discovering，annotating，and recognizing scene attributes[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition，2012：2751-2758.
[82] ROHRBACH M，STARK M，SZARVAS G，et al.What helps where-and why? semantic relatedness for knowledge transfer[C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition，2010：910-917.
[83] ROHRBACH M，STARK M，SCHIELE B.Evaluating knowledge transfer and zero-shot learning in a large-scale setting[C]//2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition，2011：1641-1648.
[84] FU Y，SIGAL L.Semi-supervised vocabulary-informed learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2016：5337-5346.
[85] SOOMRO K，ZAMIR A R，SHAH M.UCF101：a dataset of 101 human actions classes from videos in the wild[J].arXiv：1212.0402，2012.
[86] IDREES H，ZAMIR A R，JIANG Y G，et al.The THUMOS challenge on action recognition for videos “in the wild”[J].Computer Vision and Image Understanding，2017，155：1-23.
[87] CABA HEILBRON F，ESCORCIA V，GHANEM B，et al.Activitynet：A large-scale video benchmark for human activity understanding[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2015：961-970.
[88] JIANG Y G，YE G，CHANG S F，et al.Consumer video understanding：A benchmark database and an evaluation of human and machine performance[C]//Proceedings of the 1st ACM International Conference on Multimedia Retrieval，2011：1-8.
[89] 刘欢，郑庆华，罗敏楠，等.基于跨域对抗学习的零样本分类[J].计算机研究与发展，2019，56（12）：2521-2535.
LIU H，ZHENG Q H，LUO M N，et al.Zero-shot classification based on cross domain adversarial learning[J].Journal of Computer Research and Development，2019，56（12）：2521-2535.
[90] FU Y，HOSPEDALES T M，XIANG T，et al.Attribute learning for understanding unstructured social activity[C]//European Conference on Computer Vision，2012：530-543.
[91] LI H，LI D，LUO X.Bap：Bimodal attribute prediction for zero-shot image categorization[C]//Proceedings of the 22nd ACM International Conference on Multimedia，2014：1013-1016.
[92] HUANG S，ELHOSEINY M，ELGAMMAL A，et al.Learning hypergraph-regularized attribute predictors[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2015：409-417.
[93] ROMERA-PAREDES B，TORR P.An embarrassingly simple approach to zero-shot learning[C]//International Conference on Machine Learning，2015：2152-2161.
[94] NIU L，CAI J，VEERARAGHAVAN A，et al.Zero-shot learning via category-specific visual-semantic mapping and label refinement[J].IEEE Transactions on Image Processing，2018，28（2）：965-979.
[95] FROME A，CORRADO G，SHLENS J，et al.DeViSE：A deep visual-semantic embedding model[C]//Proceedings of the 26th International Conference on Neural Information Processing Systems，2013.
[96] ZHANG L，HUANG S，LIU W.Intra-class part swapping for fine-grained image classification[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision，2021：3209-3218.
[97] AKATA Z，PERRONNIN F，HARCHAOUI Z，et al.Label-embedding for image classification[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2016，38（7）：1425-1438.
[98] AKATA Z，REED S，WALTER D，et al.Evaluation of output embeddings for fine?grained image classification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2015：2927-2936.
[99] XIAN Y，AKATA Z，SHARMA G，et al.Latent embeddings for zero-shot classification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2016：69-77.
[100] DINH L，KRUEGER D，BENGIO Y.Nice：Non-linear independent components estimation[J].arXiv：1410.8516，2014.
[101] DINH L，SOHL-DICKSTEIN J，BENGIO S.Density estimation using real NVP[J].arXiv：1605.08803，2016.
[102] KINGMA D P，DHARIWAL P.Glow：Generative flow with invertible 1x1 convolutions[J].arXiv：1807.03039，2018.
[103] YAZDANI M，HENDERSON J.A model of zero-shot learning of spoken language understanding[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing，2015：244-249.
[104] VEERANNA S P，NAM J，MENCIA E L，et al.Using semantic similarity for multi-label zero-shot classification of text documents[C]//Proceeding of European Symposium on Artificial Neural Networks，Computational Intelligence and Machine Learning，2016：423-428.
[105] CHEN B，DENG W.Hybrid-attention based decoupled metric learning for zero-shot image retrieval[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：2750-2759.
[106] KUMAR N，BERG A C，BELHUMEUR P N，et al.Attribute and simile classifiers for face verification[C]//2009 IEEE 12th International Conference on Computer Vision，2009：365-372.
[107] 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，2013，36（3）：453-465.
[108] KORDUMOVA S，MENSINK T，SNOEK C G M.Pooling objects for recognizing scenes without examples[C]//Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval，2016：143-150.
[109] BUCHER M，VU T H，CORD M，et al.Zero-shot semantic segmentation[J].arXiv：1906.00817，2019.
[110] LIU J，KUIPERS B，SAVARESE S.Recognizing human actions by attributes[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2011：3337-3344.
[111] METTES P，SNOEK C G M.Spatial-aware object embeddings for zero-shot localization and classification of actions[C]//Proceedings of the IEEE International Conference on Computer Vision，2017：4443-4452.
[112] 蔡禄汀.基于零样本学习的视频图像超分辨技术研究[D].成都：电子科技大学，2020.
CAI L T.Research on video image super resolution technology based on zero-shot learning[D].Chengdu：University of Electronic Science and Technology of China，2020.
[113] KAN M，SHAN S，CHEN X.Multi-view deep network for cross-view classification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2016：4847-4855.
[114] CHAO G，SUN S.Consensus and complementarity based maximum entropy discrimination for multi-view classification[J].Information Sciences，2016，367/368：296-310.
[115] ZHAO B，WU X，CHENG Z Q，et al.Multi-view image generation from a single-view[C]//Proceedings of the 26th ACM International Conference on Multimedia，2018：383-391.
[116] SWOGER J，VERVEER P，GREGER K，et al.Multi-view image fusion improves resolution in three-dimensional microscopy[J].Optics Express，2007，15（13）：8029-8042.
[117] POLIKAR R.Ensemble learning[M]//Ensemble machine learning.Boston，MA：Springer，2012：1-34.
[118] ARAQUE O，CORCUERA-PLATAS I，SáNCHEZ-RADA J F，et al.Enhancing deep learning sentiment analysis with ensemble techniques in social applications[J].Expert Systems with Applications，2017，77：236-246.
[119] ZHANG D，MA J，YI J，et al.An ensemble method for unbalanced sentiment classification[C]//Proceedings of the 11th International Conference on Natural Computation（ICNC），2015：440-445.
[120] SHI L，MA X，XI L，et al.Rough set and ensemble learning based semi-supervised algorithm for text classification[J].Expert Systems with Applications，2011，38（5）：6300-6306.
[121] LIANG D，YI B.Two-stage three-way enhanced technique for ensemble learning in inclusive policy text classification[J].Information Sciences，2021，547：271-288.
[122] SU H，YU Y，DU Q，et al.Ensemble learning for hyperspectral image classification using tangent collaborative representation[J].IEEE Transactions on Geoscience and Remote Sensing，2020，58（6）：3778-3790.
[123] BI Y，XUE B，ZHANG M.An automated ensemble learning framework using genetic programming for image classification[C]//Proceedings of the Genetic and Evolutionary Computation Conference，2019：365-373.
[124] FELIX R，SASDELLI M，REID I，et al.Multi-modal ensemble classification for generalized zero shot learning[J].arXiv：1901.04623，2019.