2.765

2022影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

零样本学习研究进展

张鲁宁 左信 刘建伟

张鲁宁, 左信, 刘建伟. 零样本学习研究进展. 自动化学报, 2020, 46(1): 1-23. doi: 10.16383/j.aas.c180429
引用本文: 张鲁宁, 左信, 刘建伟. 零样本学习研究进展. 自动化学报, 2020, 46(1): 1-23. doi: 10.16383/j.aas.c180429
ZHANG Lu-Ning, ZUO Xin, LIU Jian-Wei. Research and Development on Zero-Shot Learning. ACTA AUTOMATICA SINICA, 2020, 46(1): 1-23. doi: 10.16383/j.aas.c180429
Citation: ZHANG Lu-Ning, ZUO Xin, LIU Jian-Wei. Research and Development on Zero-Shot Learning. ACTA AUTOMATICA SINICA, 2020, 46(1): 1-23. doi: 10.16383/j.aas.c180429

零样本学习研究进展

doi: 10.16383/j.aas.c180429
基金项目: 

国家重点研发计划项目 2016YFC0303703-03

中国石油大学(北京)年度前瞻导向及培育项目 2462018QZDX02

详细信息
    作者简介:

    张鲁宁   中国石油大学(北京)自动化系博士研究生.2016年获得中国石油大学(北京)自动化系学士学位.主要研究方向为零样本学习与点过程学习. E-mail:zhang.luning@163.com

    刘建伟  中国石油大学(北京)自动化系副研究员.主要研究方向为模式识别与智能系统, 先进控制. E-mail:liujw@cup.edu.cn

    通讯作者:

    左信  中国石油大学(北京)自动化系教授.主要研究方向为智能控制, 安全仪表系统的分析和设计, 先进过程控制.本文通信作者.E-mail:zuox@cup.edu.cn

Research and Development on Zero-Shot Learning

Funds: 

National Key Research and Development Program 2016YFC0303703-03

China University of Petroleum (Beijing) Prospective Orientation and Cultivation Project 2462018QZDX02

More Information
    Author Bio:

    ZHANG Lu-Ning   Ph. D. candidate in the Department of Automation, China University of Petroleum (Beijing).He received his bachelor degree from the Department of Automation, China University of Petroleum (Beijing) in 2016. His research interest covers zero-shot learning and point-process learning

    LIU Jian-Wei   Associate researcher in the Department of Automation, China University of Petroleum (Beijing).His research interest covers pattern recognition and intelligent system, and advanced control

    Corresponding author: ZUO Xin   Professor in the Department of Automation, China University of Petroleum (Beijing). His research interest covers intelligent control, analysis and design of safety instumented system, and advanced process control. Corresponding author of this paper
  • 摘要: 近几年来, 深度学习在机器学习研究领域中取得了巨大的突破, 深度学习能够很好地实现复杂问题的学习, 然而, 深度学习最大的弊端之一, 就是需要大量人工标注的训练数据, 而这需要耗费大量的人力成本.因此, 为了缓解深度学习存在的这一问题, Palatucci等于2009年提出了零样本学习(Zero-shot learning).零样本学习是迁移学习的一种特殊场景, 在零样本学习过程中, 训练类集和测试类集之间没有交集, 需要通过训练类与测试类之间的知识迁移来完成学习, 使在训练类上训练得到的模型能够成功识别测试类输入样例的类标签.零样本学习的意义不仅在于可以对难以标注的样例进行识别, 更在于这一方法模拟了人类对于从未见过的对象的认知过程, 零样本学习方法的研究, 也会在一定程度上促进认知科学的研究.鉴于零样本学习的应用价值、理论意义和未来的发展潜力, 文中系统综述了零样本学习的研究进展, 首先概述了零样本学习的定义, 介绍了4种典型的零样本学习模型, 并对零样本学习存在的关键问题及解决方法进行了介绍, 对零样本学习的多种模型进行了分类和阐述, 并在最后指明了零样本学习进一步研究中需要解决的问题以及未来可能的发展方向.
    Recommended by Associate Editor ZHANG Min-Ling
    1)  本文责任编委  张敏灵
  • 图  1  零样本学习结构示意图

    Fig.  1  Zero-shot learning structure

    图  2  输入空间方法示意图

    Fig.  2  Input space method

    图  3  模型空间方法示意图

    Fig.  3  Model space method

    图  4  语义输出编码零样本学习过程示意图

    Fig.  4  Semantic output code zero-shot learning process

    图  5  直接属性预测模型结构示意图

    Fig.  5  Direct attribute prediction model

    图  6  间接属性预测模型结构示意图

    Fig.  6  Indirect attribute prediction model

    图  7  跨模态迁移零样本学习示意图

    Fig.  7  Cross-modal zero-shot learning

    图  8  枢纽化问题示意图

    Fig.  8  Hubness

    图  9  映射域偏移问题示意图

    Fig.  9  The projection domain shift problem

    图  10  相容性模型示意图

    Fig.  10  Compatibility model

    图  11  混合模型示意图

    Fig.  11  Hybrid model

    图  12  线性相容性模型分类示意图

    Fig.  12  Linear compatibility model classification

    图  13  非线性相容性模型分类示意图

    Fig.  13  Nonlinear compatibility model classification

    图  14  混合模型分类示意图

    Fig.  14  Hybrid model classification

    图  15  语义自编码器零样本学习示意图

    Fig.  15  Semantic autoencoder zero-shot learning

    图  16  语义相似嵌入模型零样本学习示意图

    Fig.  16  Semantic similarity embedding zero-shot learning model

    表  1  5种数据集属性介绍

    Table  1  Introduction to the attributes of the five datasets

    数据集 AWA CUB aPY SUN AwA2
    图像个数 30 475 11788 15 539 14 340 37 322
    类个数 50 200 32 17 50
    属性个数 85 312 64 102 85
    注释水平 每一类 每张图片 每张图片 每张图片 每一类
    注释类型(实值或布尔值) 兼有 兼有 兼有 布尔 兼有
    下载: 导出CSV

    表  2  多个模型在4个数据集下的实验结果

    Table  2  Experimental results of the models under four data

    模型一数据集(%) AWA CUB aPY SUN
    DAP[14] 41.4 28.3 \ 19.1
    IAP[14] 42.2 24.4 \ 16.9
    ESZSL[23] 49.3 \ 65.8*\18.7 15.1
    SYNC[46] 69.7 53.4 62.8 \
    SSE[64] 76.3 30.4 82.5* 46. 2
    LATEM[62] 71.9 45. 5 \ \
    SJE[57] 66.7 50.1 56.1 \
    SAE[59] 84.7 61.4 91.0*\65.2 54.8
    下载: 导出CSV
  • [1] Schölkopf B, Smola A J. Learning with Kernels. Cambridge: MIT Press, 2001.
    [2] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks. In: Proceedings of Advances in Neural Information Processing Systems. Lake Tahoe, USA: MIT Press, 2012. 1097-1105
    [3] Gregor K, Danihelka I, Graves A, Rezende D J, Wierstra D. DRAW: a recurrent neural network for image generation. arXiv preprint arXiv: 1502.04623, 2015. http://cn.bing.com/academic/profile?id=046d1bffdbfb5068f065d8bdf9403628&encoded=0&v=paper_preview&mkt=zh-cn
    [4] Biederman I. Recognition-by-components: a theory of human image understanding. Psychological Review, 1987, 94(2): 115-147 doi: 10.1037/0033-295X.94.2.115
    [5] Yao B P, Khosla A, Li F F. Combining randomization and discrimination for fine-grained image categorization. In: Proceedings of the Computer Vision and Pattern Recognition. Providence, RI, USA: IEEE, 2011. 1577-1584
    [6] Murphy G L. The Big Book of Concepts. Cambridge: MIT Press, 2004. https://mitpress.mit.edu/books/big-book-concepts
    [7] Koggalage R, Halgamuge S K. Reducing the number of training samples for fast support vector machine classification. Neural Information Processing, 2004, 2(3): 57-65 http://cn.bing.com/academic/profile?id=d54b53441cc53c03e26037d670f4bc72&encoded=0&v=paper_preview&mkt=zh-cn
    [8] Li F F, Fergus R, Perona P. One-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(4): 594-611 doi: 10.1109/TPAMI.2006.79
    [9] Santoro A, Bartunov S, Botvinick M, Wierstra D, Lillicrap T. One-shot learning with memory-augmented neural networks. arXiv preprint arXiv: 1605.06065, 2016.
    [10] Fanello S R, Gori I, Metta G, Odone F. One-shot learning for real-time action recognition. In: Proceedings of Pattern Recognition and Image Analysis. Berlin, Heidelberg: Springer, 2013. 31-40
    [11] Pan S J, Yang Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359 doi: 10.1109/TKDE.2009.191
    [12] Bakker B, Heskes T. Task clustering and gating for Bayesian multitask learning. Journal of Machine Learning Research, 2003, 4(12): 83-99 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=065e5313fe59658a6e44688df00aff1b
    [13] Bonilla E V, Agakov F V, Williams C K I. Kernel multi-task learning using task-specific features. In: Proceedings of the 11th International Conference on Artificial Intelligence and Statistics, Atherton, USA: PMLR, 2007. 43-50
    [14] Lampert C H, Nickisch H, Harmeling S. Learning to detect unseen object classes by between-class attribute transfer. In: Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA: IEEE, 2009. 951-958
    [15] Palatucci M, Pomerleau D, Hinton G, Mitchell T M. Zero-shot learning with semantic output codes. In: Proceedings of the 22nd International Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada: Curran Associates Inc., 2009. 1410-1418
    [16] Ba J L, Swersky K, Fidler S, Salakhutdinov R. Predicting deep zero-shot convolutional neural networks using textual descriptions. In: Proceedings of the 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015. 4247-4255
    [17] Zhang L, Xiang T, Gong S G. Learning a deep embedding model for zero-shot learning. arXiv preprint arXiv: 1611.05088, 2016.
    [18] Zhang D, Liu Y, Si L. Serendipitous learning: learning beyond the predefined label space. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, USA: ACM, 2011. 1343-1351 http://cn.bing.com/academic/profile?id=a5c71403e19916c25cb401816c1c4b16&encoded=0&v=paper_preview&mkt=zh-cn
    [19] Du C, Zhuang F, He J, He Q, Long G. Learning beyond predefined label space via bayesian nonparametric topic modelling. In: Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Cham, Riva del Garda, Italy: Springer, 2016. 148-164
    [20] Zhuang F Z, Luo P, Shen Z Y, He Q, Xiong Y H, Shi Z Z. D-LDA: a topic modeling approach without constraint generation for semi-defined classification. In: Proceedings of the 2010 IEEE International Conference on Data Mining. Sydney, Australia: IEEE, 2010. 709-718
    [21] Larochelle H, Erhan D, Bengio Y. Zero-data learning of new tasks. In: Proceedings of the 23rd AAAI Conference on Artificial Intelligence. Chicago, USA: AAAI, 2013. 646-651
    [22] Socher R, Ganjoo M, Sridhar H, Bastani O, Manning C D, Ng A Y. Zero-shot learning through cross-modal transfer. In: Proceedings of the Advances in Neural Information Processing Systems. Lake Tahoe, USA: MIT Press, 2013. 935-943
    [23] Romera-Paredes B, Torr P H S. An embarrassingly simple approach to zero-shot learning. In: Proceedings of the 32nd International Conference on Machine Learning. Lille, France: ACM, 2015. 2152-2161
    [24] Qiao R Z, Liu L Q, Shen C H, van den Hengel A. Less is more: zero-shot learning from online textual documents with noise suppression. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016. 2249-2257
    [25] Dietterich T G, Bakiri G. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 1994, 2: 263-286 http://d.old.wanfangdata.com.cn/OAPaper/oai_arXiv.org_cs%2f9501101
    [26] Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. New York: Springer, 2001.
    [27] Sloman S A. Feature-based induction. Cognitive Psychology, 1993, 25(2): 231-280 doi: 10.1006/cogp.1993.1006
    [28] Osherson D, Smith E E, Myers T S, Shafir E, Stob M. Extrapolating human probability judgment. Theory & Decision, 1994, 36(2): 103-129 http://cn.bing.com/academic/profile?id=1acd40713768044e31d5634955b76578&encoded=0&v=paper_preview&mkt=zh-cn
    [29] Ferrari V, Zisserman A. Learning visual attributes. In: Proceedings of the 21st Annual Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada: Curran Associates Inc., 2007. 433-440
    [30] van de Weijer J, Schmid C, Verbeek J. Learning color names from real-world images. In: Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, Minnesota, USA: IEEE, 2007. 1-8
    [31] Yanai K, Barnard K. Image region entropy: a measure of "visualness" of web images associated with one concept. In: Proceedings of the 13th Annual ACM International Conference on Multimedia. New York, USA: ACM, 2005. 419-422
    [32] Farhadi A, Endres I, Hoiem D, Forsyth D. Describing objects by their attributes. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR). 2009. Miami Beach, USA: IEEE, 2009. 1778-1785
    [33] Lampert C H, Nickisch H, Harmeling S. Attribute-based classification for zero-shot visual object categorization. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2014, 36(3): 453-465 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=485cb3a8f8e0a8984bad0fe6069ea2d9
    [34] Suzuki M, Sato H, Oyama S, Kurihara M. Transfer learning based on the observation probability of each attribute. In: Proceedings of the 2014 IEEE International Conference on Systems, Man, and Cybernetics. San Diego, USA: IEEE, 2014. 3627-3631
    [35] Kovashka A, Parikh D, Grauman K. WhittleSearch: image search with relative attribute feedback. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA: IEEE, 2012. 2973-2980
    [36] Parkash A, Parikh D. Attributes for classifier feedback. In: Proceedings of the European Conference on Computer Vision. Berlin, Heidelberg: Springer, 2012. 354-368
    [37] Kulkarni G, Premraj V, Dhar S, Li S M, Choi Y J, Berg A C, et al. Baby talk: understanding and generating simple image descriptions. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Colorado Springs, USA: IEEE, 2011. 1601-1608
    [38] Kumar N, Berg A, Belhumeur P N, Nayar S. Describable visual attributes for face verification and image search. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2011, 33(10): 1962-1977 doi: 10.1109-TPAMI.2011.48/
    [39] Liu J, Kuipers B, Savarese S. Recognizing human actions by attributes. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Colorado Springs, USA: IEEE, 2011. 3337-3344
    [40] Patterson G, Hays J. SUN attribute database: discovering, annotating, and recognizing scene attributes. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA: IEEE, 2012. 2751-2758
    [41] Feris R, Siddiquie B, Zhai Y, Petterson J, Brown L, Pankanti S. Attribute-based vehicle search in crowded surveillance videos. In: Proceedings of the 1st ACM International Conference on Multimedia Retrieval. Trento, Italy: ACM, 2011. Article No. 18
    [42] Fu Y W, Hospedales T M, Xiang T, Fu Z Y, Gong S G. Transductive multi-view embedding for zero-shot recognition and annotation. In: Proceedings of European Conference on Computer Vision. Zurich, Switzerland: Springer, 2014. 584-599
    [43] Chao W L, Changpinyo S, Gong B, Sha F. An empirical study and analysis of generalized zero-shot learning for object recognition in the wild. In: Proceedings of European Conference on Computer Vision. Amsterdam, The Netherlands: Springer, 2016. 52-68
    [44] Lazaridou A, Dinu G, Baroni M. Hubness and pollution: delving into cross-space mapping for zero-shot learning. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. Beijing, China: ACL, 2015. 270-280
    [45] Norouzi M, Mikolov T, Bengio S, Singer Y, Shlens J, Frome A, et al. Zero-shot learning by convex combination of semantic embeddings. arXiv preprint arXiv: 1312.5650, 2013
    [46] Changpinyo S, Chao W L, Gong B Q, Sha F. Synthesized classifiers for zero-shot learning. In: Proceedings of the 2016 IEEE Conference on Computer vision and pattern recognition. Las Vegas, USA: IEEE, 2016. 5327-5336
    [47] Radovanović M, Nanopoulos A, Ivanović M. Hubs in space: popular nearest neighbors in high-dimensional data. Journal of Machine Learning Research, 2010, 11: 2487-2531 http://cn.bing.com/academic/profile?id=cd255ab6835ff4b1191724b737470cd3&encoded=0&v=paper_preview&mkt=zh-cn
    [48] Radovanović M, Nanopoulos A, Ivanović M. On the existence of obstinate results in vector space models. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval. Geneva, Switzerland: ACM, 2010. 186-193
    [49] Dinu G, Lazaridou A, Baroni M. Improving zero-shot learning by mitigating the hubness problem. arXiv preprint arXiv: 1412.6568, 2014
    [50] Kodirov E, Xiang T, Fu Z Y, Gong S G. Unsupervised domain adaptation for zero-shot learning. In: Proceedings of the 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015. 2452-2460
    [51] Harris Z S. Distributional structure. Word, 1954, 10(2-3): 146-162 doi: 10.1080/00437956.1954.11659520
    [52] Mikolov T, Sutskever I, Chen K, Corrado G, Dean J. Distributed representations of words and phrases and their compositionality. In: Proceedings of Advances in Neural Information Processing Systems. Lake Tahoe, USA: MIT Press, 2013. 3111-3119
    [53] Pennington J, Socher R, Manning C D. GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha, Qatar: ACL, 2014. 1532-1543 http://cn.bing.com/academic/profile?id=537a8511c9ae20478a13d76f2a1e7035&encoded=0&v=paper_preview&mkt=zh-cn
    [54] Blanchard E, Harzallah M, Briand H, Kuntz P. A typology of ontology-based semantic measures. In: EMOI-INTEROP. Portugal: Springer, 2005. 160
    [55] Frome A, Corrado G S, Shlens J, Bengio S, Dean J, Ranzato M, et al. DeViSE: a deep visual-semantic embedding model. In: Proceedings of Advances in Neural Information Processing Systems. Lake Tahoe, USA: MIT Press, 2013. 2121-2129
    [56] Akata Z, Perronnin F, Harchaoui Z, Schmid C. Label-embedding for attribute-based classification. In: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA: IEEE, 2013. 819-826
    [57] Akata Z, Reed S, Walter D, Lee H, Schiele B. Evaluation of output embeddings for fine-grained image classification. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, USA: IEEE, 2015. 2927-2936
    [58] Reed S, Akata Z, Lee H, Schiele B. Learning deep representations of fine-grained visual descriptions. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016. 49-58
    [59] Kodirov E, Xiang T, Gong S G. Semantic autoencoder for zero-shot learning. arXiv preprint arXiv: 1704.08345, 2017
    [60] Bucher M, Herbin S, Jurie F. Improving semantic embedding consistency by metric learning for zero-shot classiffication. In: Proceedings of European Conference on Computer Vision. Amsterdam, The Netherlands: Springer, 2016. 730-746
    [61] Zhang Z M, Saligrama V. Zero-shot learning via joint latent similarity embedding. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016. 6034-6042
    [62] Xian Y Q, Akata Z, Sharma G, Nguyen Q, Hein M, Schiele B. Latent embeddings for zero-shot classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016. 69-77
    [63] Jayaraman D, Grauman K. Zero-shot recognition with unreliable attributes. In: Proceedings of the International Conference on Neural Information Processing Systems. Montreal, Canada: MIT Press, 2014. 3464-3472
    [64] Zhang Z M, Saligrama V. Zero-shot learning via semantic similarity embedding. In: Proceedings of the 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE 2015. 4166-4174
    [65] Zhao B, Wu B T, Wu T F, Wang Y Z. Zero-shot learning posed as a missing data problem. arXiv preprint arXiv: 1612.00560, 2016
    [66] Zhang Z M, Saligrama V. Zero-shot recognition via structured prediction. In: European Conference on Computer Vision. Amsterdam, The Netherlands: Springer, 2016. 533-548
    [67] Wang D H, Li Y, Lin Y T, Zhuang Y T. Relational knowledge transfer for zero-shot learning. In: Proceedings of the 13th AAAI Conference on Artificial Intelligence. Phoenix, USA: AAAI, 2016. 2145-2151
    [68] Luo C Z, Li Z T, Huang K Z, Feng J S, Wang M. Zero-shot learning via attribute regression and class prototype rectification. IEEE Transactions on Image Processing, 2018, 27(2): 637-648 http://cn.bing.com/academic/profile?id=cc3cb532b2789d33ffe0a8326dbeeec3&encoded=0&v=paper_preview&mkt=zh-cn
    [69] Hinton G, Vinyals O, Dean J. Distilling the knowledge in a neural network. In: Advances in Neural Information Processing Systems 27. Montreal, Canada: MIT Press, 2014. 1-9
    [70] Lu Y. Unsupervised learning on neural network outputs: with application in zero-shot learning. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence. New York, USA: AAAI, 2016. 3432-3438
    [71] Baldi P, Hornik K. Neural networks and principal component analysis: learning from examples without local minima. Neural Networks, 1989, 2(1): 53-58 http://cn.bing.com/academic/profile?id=71193a90089a19100017f3263a421ddb&encoded=0&v=paper_preview&mkt=zh-cn
    [72] Hyvarinen A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 1999, 10(3): 626-634 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=df2ada21711f3db3a676da39586b8210
    [73] Deng J, Dong W, Socher R, Li L J, Li K, Fei-Fei L. ImageNet: a large-scale hierarchical image database. In: Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami Beach, USA: IEEE, 2009. 248-255
    [74] Song J, Shen C C, Yang Y Z, Liu Y, Song M L. Transductive unbiased embedding for zero-shot learning. In: Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE, 2018. 1024-1033
    [75] Guo Y C, Ding G G, Jin X M, Wang J M. Transductive zero-shot recognition via shared model space learning. In: Proceedings of the 13th AAAI Conference on Artificial Intelligence. Phoenix, USA: AAAI Press, 2016. 3434-3500
    [76] Xian Y Q, Lampert C H, Schiele B, Akata Z. Zero-shot learning-A comprehensive evaluation of the good, the bad and the ugly. arXiv preprint arXiv: 1707.00600, 2017
    [77] Wah C, Branson S, Welinder P, Perona P, Belongie S. The caltech-UCSD birds-200-2011 dataset, Computation & Neural Systems Technical Report, California Institute of Technology, Pasadena, CA, 2011. https://authors.library.caltech.edu/27452/1/CUB_200_2011.pdf
    [78] Xiao J X, Hays J, Ehinger K A, Oliva A, Torralba A. Sun database: large-scale scene recognition from abbey to zoo. In: Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, USA: IEEE, 2010. 3485-3492
    [79] Antol S, Zitnick C L, Parikh D. Zero-shot learning via visual abstraction. In: Proceedings of the European Conference on Computer Vision. Zurich, Switzerland: Springer, 2014. 401-416
    [80] Robyns P, Marin E, Lamotte W, Quax P, Singelée D, Preneel B. Physical-layer fingerprinting of LoRa devices using supervised and zero-shot learning. In: Proceedings of the 10th ACM Conference on Security and Privacy in Wireless and Mobile Networks. Boston, Massachusetts: ACM, 2017. 58-63
    [81] Yang Y, Luo Y D, Chen W L, Shen F M, Shao J, Shen H T. Zero-shot hashing via transferring supervised knowledge. In: Proceedings of the 24th ACM International Conference on Multimedia. Amsterdam, The Netherlands: ACM, 2016. 1286-1295
    [82] Johnson M, Schuster M, Le Q V, Krikun M, Wu Y H, Chen Z F, et al. Google's multilingual neural machine translation system: enabling zero-shot translation. arXiv preprint arXiv: 1611.04558, 2016.
    [83] Veeranna S P, Nam J, Mencía E L, Furnkranz J. Using semantic similarity for multi-label zero-shot classification of text documents. In: Proceeding of European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium: Elsevier, 2016. 423-428
  • 加载中
图(16) / 表(2)
计量
  • 文章访问数:  6644
  • HTML全文浏览量:  2796
  • PDF下载量:  1856
  • 被引次数: 0
出版历程
  • 收稿日期:  2018-06-15
  • 录用日期:  2018-08-30
  • 刊出日期:  2020-01-21

目录

    /

    返回文章
    返回