survey

A Survey of Zero-Shot Learning: Settings, Methods, and Applications

Authors:
Wei Wang

Nanyang Technological University, Nanyang Avenue, Singapore

Nanyang Technological University, Nanyang Avenue, Singapore

0000-0001-7283-8652
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,
Vincent W. Zheng

WeBank, Shahexilu, Nanshan, Shenzhen, China

WeBank, Shahexilu, Nanshan, Shenzhen, China
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,
Han Yu

Nanyang Technological University, Nanyang Avenue, Singapore

Nanyang Technological University, Nanyang Avenue, Singapore
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,
Chunyan Miao

Nanyang Technological University, Nanyang Avenue, Singapore

Nanyang Technological University, Nanyang Avenue, Singapore
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ACM Transactions on Intelligent Systems and Technology Volume 10 Issue 2Article No.: 13pp 1–37https://doi.org/10.1145/3293318

Published:16 January 2019Publication History

ACM Transactions on Intelligent Systems and Technology

Abstract

Most machine-learning methods focus on classifying instances whose classes have already been seen in training. In practice, many applications require classifying instances whose classes have not been seen previously. Zero-shot learning is a powerful and promising learning paradigm, in which the classes covered by training instances and the classes we aim to classify are disjoint. In this paper, we provide a comprehensive survey of zero-shot learning. First of all, we provide an overview of zero-shot learning. According to the data utilized in model optimization, we classify zero-shot learning into three learning settings. Second, we describe different semantic spaces adopted in existing zero-shot learning works. Third, we categorize existing zero-shot learning methods and introduce representative methods under each category. Fourth, we discuss different applications of zero-shot learning. Finally, we highlight promising future research directions of zero-shot learning.

References

Zeynep Akata, Mateusz Malinowski, Mario Fritz, and Bernt Schiele. 2016. Multi-cue zero-shot learning with strong supervision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). 59--68.Google ScholarCross Ref
Zeynep Akata, Florent Perronnin, Zaid Harchaoui, and Cordelia Schmid. 2013. Label-embedding for attribute-based classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’13). 819--826. Google ScholarDigital Library
Zeynep Akata, Florent Perronnin, Zaid Harchaoui, and Cordelia Schmid. 2016. Label-embedding for image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 38, 7 (2016), 1425--1438.Google ScholarCross Ref
Zeynep Akata, Scott Reed, Daniel Walter, Honglak Lee, and Bernt Schiele. 2015. Evaluation of output embeddings for fine-grained image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15). 2927--2936.Google ScholarCross Ref
Ziad Al-Halah and Rainer Stiefelhagen. 2015. How to transfer? Zero-shot object recognition via hierarchical transfer of semantic attributes. In IEEE Winter Conference on Applications of Computer Vision (WACV’15). 837--843. Google ScholarDigital Library
Ibrahim Alabdulmohsin, Moustapha Cisse, and Xiangliang Zhang. 2016. Is attribute-based zero-shot learning an Ill-posed strategy? In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD’16). 749--760. Google ScholarDigital Library
Stanislaw Antol, C. Lawrence Zitnick, and Devi Parikh. 2014. Zero-shot learning via visual abstraction. In European Conference on Computer Vision (ECCV’14). 401--416.Google ScholarCross Ref
Jimmy Lei Ba, Kevin Swersky, Sanja Fidler, and Ruslan Salakhutdinov. 2015. Predicting deep zero-shot convolutional neural networks using textual descriptions. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’15). 4247--4255. Google ScholarDigital Library
Abhijit Bendale and Terrance Boult. 2015. Towards open world recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15). 1893--1902.Google ScholarCross Ref
Irving Biederman. 1987. Recognition-by-components: A theory of human image understanding. Psychological Review 94, 2 (1987), 115--147.Google ScholarCross Ref
Maxime Bucher, Stéphane Herbin, and Frédéric Jurie. 2017. Generating visual representations for zero-shot classification. In IEEE International Conference on Computer Vision Workshops: Transferring and Adapting Source Knowledge in Computer Vision (ICCV Workshops’17). 2666--2673.Google Scholar
Carlos A. Caceres, Matthew J. Roos, Kyle M. Rupp, Griffin Milsap, Nathan E. Crone, Michael E. Wolmetz, and Christopher R. Ratto. 2017. Feature selection methods for zero-shot learning of neural activity. Frontiers in Neuroinformatics 11 (2017), 41.Google ScholarCross Ref
Spencer Cappallo, Thomas Mensink, and Cees G. M. Snoek. 2016. Video stream retrieval of unseen queries using semantic memory. In Proceedings of the British Machine Vision Conference (BMVC’16).Google Scholar
Spencer Cappallo, Thomas Mensink, and Cees G. M. Snoek. 2015. Image2Emoji: Zero-shot emoji prediction for visual media. In Proceedings of the 23rd ACM International Conference on Multimedia (MM’15). 1311--1314. Google ScholarDigital Library
Xiaojun Chang, Yi Yang, Alexander G. Hauptmann, Eric P. Xing, and Yao-Liang Yu. 2015. Semantic concept discovery for large-scale zero-shot event detection. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI’15). 2234--2240. Google ScholarDigital Library
Soravit Changpinyo, Wei-Lun Chao, Boqing Gong, and Fei Sha. 2016. Synthesized classifiers for zero-shot learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). 5327--5336.Google ScholarCross Ref
Wei-Lun Chao, Soravit Changpinyo, Boqing Gong, and Fei Sha. 2016. An empirical study and analysis of generalized zero-shot learning for object recognition in the wild. In European Conference on Computer Vision (ECCV’16). 52--68.Google ScholarCross Ref
Long Chen, Hanwang Zhang, Jun Xiao, Wei Liu, and Shih-Fu Chang. 2018. Zero-shot visual recognition using semantics-preserving adversarial embedding networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18).Google ScholarCross Ref
Heng-Tze Cheng, Martin Griss, Paul Davis, Jianguo Li, and Di You. 2013. Towards zero-shot learning for human activity recognition using semantic attribute sequence model. In Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’13). 355--358. Google ScholarDigital Library
Heng-Tze Cheng, Feng-Tso Sun, Martin Griss, Paul Davis, Jianguo Li, and Di You. 2013. NuActiv: Recognizing unseen new activities using semantic attribute-based learning. In Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys’13). 361--374. Google ScholarDigital Library
Jeffrey Dalton, James Allan, and Pranav Mirajkar. 2013. Zero-shot video retrieval using content and concepts. In Proceedings of the 22nd ACM International Conference on Information and Knowledge Management (CIKM’13). 1857--1860. Google ScholarDigital Library
Yann N. Dauphin, Gokhan Tur, Dilek Hakkani-Tür, and Larry Heck. 2014. Zero-shot learning for semantic utterance classification. In International Conference on Learning Representations (ICLR’14).Google Scholar
Oscar Day and Taghi M. Khoshgoftaar. 2017. A survey on heterogeneous transfer learning. Journal of Big Data 4, 1 (2017), 29.Google ScholarCross Ref
Berkan Demirel, Ramazan Gokberk Cinbis, and Nazli Ikizler-Cinbis. 2017. Attributes2Classname: A discriminative model for attribute-based unsupervised zero-shot learning. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’17). 1241--1250.Google ScholarCross Ref
Jia Deng, Nan Ding, Yangqing Jia, Andrea Frome, Kevin Murphy, Samy Bengio, Yuan Li, Hartmut Neven, and Hartwig Adam. 2014. Large-scale object classification using label relation graphs. In European Conference on Computer Vision (ECCV’14). 48--64.Google ScholarCross Ref
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. ImageNet: A large-scale hierarchical image database. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’09). 248--255.Google ScholarCross Ref
Zhengming Ding, Ming Shao, and Yun Fu. 2017. Low-rank embedded ensemble semantic dictionary for zero-shot learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17). 6005--6013.Google ScholarCross Ref
Georgiana Dinu and Marco Baroni. 2014. How to make words with vectors: Phrase generation in distributional semantics. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL’14). 624--633.Google ScholarCross Ref
Georgiana Dinu, Angeliki Lazaridou, and Marco Baroni. 2015. Improving zero-shot learning by mitigating the hubness problem. In International Conference on Learning Representations Workshops (ICLR Workshops’15).Google Scholar
Mohamed Elhoseiny, Jingen Liu, Hui Cheng, Harpreet Sawhney, and Ahmed Elgammal. 2016. Zero-shot event detection by multimodal distributional semantic embedding of videos. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI’16). 3478--3486. Google ScholarDigital Library
Mohamed Elhoseiny, Babak Saleh, and Ahmed Elgammal. 2013. Write a classifier: Zero-shot learning using purely textual descriptions. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’13). 2584--2591. Google ScholarDigital Library
Mohamed Elhoseiny, Yizhe Zhu, Han Zhang, and Ahmed Elgammal. 2017. Link the head to the “Beak”: Zero shot learning from noisy text description at part precision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17). 6288--6297.Google ScholarCross Ref
Ali Farhadi, Ian Endres, Derek Hoiem, and David Forsyth. 2009. Describing objects by their attributes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’09). 1778--1785.Google ScholarCross Ref
Geli Fei, Shuai Wang, and Bing Liu. 2016. Learning cumulatively to become more knowledgeable. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’16). 1565--1574. Google ScholarDigital Library
Li Fei-Fei, Rob Fergus, and Pietro Perona. 2006. One-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 4 (2006), 594--611. Google ScholarDigital Library
Emmanuel Ferreira, Bassam Jabaian, and Fabrice Lefèvre. 2015. Zero-shot semantic parser for spoken language understanding. In Proceedings of the 16th Annual Conference of the International Speech Communication Association (INTERSPEECH’15). 1403--1407.Google ScholarCross Ref
Orhan Firat, Baskaran Sankaran, Yaser Al-Onaizan, Fatos T. Yarman Vural, and Kyunghyun Cho. 2016. Zero-resource translation with multi-lingual neural machine translation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP’16). 268--277.Google ScholarCross Ref
Andrea Frome, Greg S. Corrado, Jonathon Shlens, Samy Bengio, Jeffrey Dean, Marc’Aurelio Ranzato, and Tomas Mikolov. 2013. DeViSE: A deep visual-semantic embedding model. In Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013 (NIPS’13). 2121--2129. Google ScholarDigital Library
Yanwei Fu, Timothy M. Hospedales, Tao Xiang, and Shaogang Gong. 2012. Attribute learning for understanding unstructured social activity. In European Conference on Computer Vision (ECCV’12). 530--543. Google ScholarDigital Library
Yanwei Fu, Timothy M. Hospedales, Tao Xiang, and Shaogang Gong. 2014. Learning multimodal latent attributes. IEEE Transactions on Pattern Analysis and Machine Intelligence 36, 2 (2014), 303--316. Google ScholarDigital Library
Yanwei Fu, Timothy M. Hospedales, Tao Xiang, and Shaogang Gong. 2015. Transductive multi-view zero-shot learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 37, 11 (2015), 2332--2345. Google ScholarDigital Library
Yanwei Fu and Leonid Sigal. 2016. Semi-supervised vocabulary-informed learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). 5337--5346.Google ScholarCross Ref
Yanwei Fu, Tao Xiang, Yu-Gang Jiang, Xiangyang Xue, Leonid Sigal, and Shaogang Gong. 2018. Recent advances in zero-shot recognition: Toward data-efficient understanding of visual content. IEEE Signal Processing Magazine 35, 1 (2018), 112--125.Google ScholarCross Ref
Yanwei Fu, Yongxin Yang, Timothy Hospedales, Tao Xiang, and Shaogang Gong. 2014. Transductive multi-label zero-shot learning. In Proceedings of the British Machine Vision Conference (BMVC’14).Google ScholarCross Ref
Zhenyong Fu, Tao Xiang, Elyor Kodirov, and Shaogang Gong. 2015. Zero-shot object recognition by semantic manifold distance. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15). 2635--2644.Google ScholarCross Ref
Zhenyong Fu, Tao Xiang, Elyor Kodirov, and Shaogang Gong. 2018. Zero-shot learning on semantic class prototype graph. IEEE Transactions on Pattern Analysis and Machine Intelligence 40, 8 (2018), 2009--2022.Google ScholarCross Ref
Ruka Funaki and Hideki Nakayama. 2015. Image-mediated learning for zero-shot cross-lingual document retrieval. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP’15). 585--590.Google ScholarCross Ref
Chuang Gan, Ming Lin, Yi Yang, Yueting Zhuang, and Alexander G. Hauptmann. 2015. Exploring semantic inter-class relationships (SIR) for zero-shot action recognition. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI’15). 3769--3775. Google ScholarDigital Library
Chuang Gan, Chen Sun, and Ram Nevatia. 2017. DECK: Discovering event composition knowledge from web images for zero-shot event detection and recounting in videos. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI’17). 4032--4038. Google ScholarDigital Library
Chuang Gan, Tianbao Yang, and Boqing Gong. 2016. Learning attributes equals multi-source domain generalization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). 87--97.Google ScholarCross Ref
Chuang Gan, Yi Yang, Linchao Zhu, Deli Zhao, and Yueting Zhuang. 2016. Recognizing an action using its name: A knowledge-based approach. International Journal of Computer Vision 120, 1 (2016), 61--77. Google ScholarDigital Library
Lianli Gao, Jingkuan Song, Junming Shao, Xiaofeng Zhu, and Heng Tao Shen. 2015. Zero-shot image categorization by image correlation exploration. In Proceedings of the 5th ACM on International Conference on Multimedia Retrieval (ICMR’15). 487--490. Google ScholarDigital Library
Efstratios Gavves, Thomas Mensink, Tatiana Tommasi, Cees G. M. Snoek, and Tinne Tuytelaars. 2015. Active transfer learning with zero-shot priors: Reusing past datasets for future tasks. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’15). 2731--2739. Google ScholarDigital Library
Sergio Guadarrama, Niveda Krishnamoorthy, Girish Malkarnenkar, Subhashini Venugopalan, Raymond Mooney, Trevor Darrell, and Kate Saenko. 2013. Youtube2Text: Recognizing and describing arbitrary activities using semantic hierarchies and zero-shot recognition. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’13). 2712--2719. Google ScholarDigital Library
Yuchen Guo, Guiguang Ding, Jungong Han, and Yue Gao. 2017. SitNet: Discrete similarity transfer network for zero-shot hashing. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI’17). 1767--1773. Google ScholarDigital Library
Yuchen Guo, Guiguang Ding, Jungong Han, and Yue Gao. 2017. Synthesizing samples for zero-shot learning. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI’17). 1774--1780. Google ScholarDigital Library
Yuchen Guo, Guiguang Ding, Jungong Han, and Yue Gao. 2017. Zero-shot learning with transferred samples. IEEE Transactions on Image Processing 26, 7 (2017), 3277--3290. Google ScholarDigital Library
Yuchen Guo, Guiguang Ding, Jungong Han, and Yue Gao. 2017. Zero-shot recognition via direct classifier learning with transferred samples and pseudo labels. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI’17). 4061--4067. Google ScholarDigital Library
Yuchen Guo, Guiguang Ding, Jungong Han, and Sheng Tang. 2018. Zero-shot learning with attribute selection. In Thirty-Second AAAI Conference on Artificial Intelligence (AAAI’18).Google Scholar
Yuchen Guo, Guiguang Ding, Xiaoming Jin, and Jianmin Wang. 2016. Transductive zero-shot recognition via shared model space learning. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI’16). Google ScholarDigital Library
Irina Higgins, Arka Pal, Andrei Rusu, Loic Matthey, Christopher Burgess, Alexander Pritzel, Matthew Botvinick, Charles Blundell, and Alexander Lerchner. 2017. DARLA: Improving zero-shot transfer in reinforcement learning. In Proceedings of the 34th International Conference on Machine Learning (ICML’17). 1480--1490. Google ScholarDigital Library
Sheng Huang, Mohamed Elhoseiny, Ahmed Elgammal, and Dan Yang. 2015. Learning hypergraph-regularized attribute predictors. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15). 409--417.Google ScholarCross Ref
David Isele, Mohammad Rostami, and Eric Eaton. 2016. Using task features for zero-shot knowledge transfer in lifelong learning. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI’16). 1620--1626. Google ScholarDigital Library
Lalit P. Jain, Walter J. Scheirer, and Terrance E. Boult. 2014. Multi-class open set recognition using probability of inclusion. In European Conference on Computer Vision (ECCV’14). 393--409.Google Scholar
Mihir Jain, Jan C. van Gemert, Thomas Mensink, and Cees G. M. Snoek. 2015. Objects2action: Classifying and localizing actions without any video example. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’15). 4588--4596. Google ScholarDigital Library
Dinesh Jayaraman and Kristen Grauman. 2014. Zero-shot recognition with unreliable attributes. In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems (NIPS’14). 3464--3472. Google ScholarDigital Library
Dinesh Jayaraman, Fei Sha, and Kristen Grauman. 2014. Decorrelating semantic visual attributes by resisting the urge to share. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’14). 1629--1636. Google ScholarDigital Library
Huajie Jiang, Ruiping Wang, Shiguang Shan, Yi Yang, and Xilin Chen. 2017. Learning discriminative latent attributes for zero-shot classification. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’17). 4233--4242.Google ScholarCross Ref
Melvin Johnson, Mike Schuster, Quoc V. Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda Viégas, Martin Wattenberg, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2017. Google’s multilingual neural machine translation system: Enabling zero-shot translation. Transactions of the Association for Computational Linguistics 5 (2017), 339--351.Google ScholarCross Ref
Pichai Kankuekul, Aram Kawewong, Sirinart Tangruamsub, and Osamu Hasegawa. 2012. Online incremental attribute-based zero-shot learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’12). 3657--3664. Google ScholarDigital Library
Ken Kansky, Tom Silver, David A. Mély, Mohamed Eldawy, Miguel Lázaro-Gredilla, Xinghua Lou, Nimrod Dorfman, Szymon Sidor, Scott Phoenix, and Dileep George. 2017. Schema networks: Zero-shot transfer with a generative causal model of intuitive physics. In Proceedings of the 34th International Conference on Machine Learning (ICML’17). 1809--1818. Google ScholarDigital Library
Nour Karessli, Zeynep Akata, Bernt Schiele, and Andreas Bulling. 2017. Gaze embeddings for zero-shot image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17). 6412--6421.Google ScholarCross Ref
Aram Kawewong, Sirinart Tangruamsub, Pichai Kankuekul, and Osamu Hasegawa. 2011. Fast online incremental transfer learning for unseen object classification using self-organizing incremental neural networks. In The International Joint Conference on Neural Networks (IJCNN’11). 749--756.Google ScholarCross Ref
Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations (ICLR’17).Google Scholar
Elyor Kodirov, Tao Xiang, Zhenyong Fu, and Shaogang Gong. 2015. Unsupervised domain adaptation for zero-shot learning. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’15). 2452--2460. Google ScholarDigital Library
Elyor Kodirov, Tao Xiang, and Shaogang Gong. 2017. Semantic autoencoder for zero-shot learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17). 4447--4456.Google ScholarCross Ref
Svetlana Kordumova, Thomas Mensink, and Cees G. M. Snoek. 2016. Pooling objects for recognizing scenes without examples. In Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval (ICMR’16). 143--150. Google ScholarDigital Library
Hildegard Kuehne, Hueihan Jhuang, Estíbaliz Garrote, Tomaso Poggio, and Thomas Serre. 2011. HMDB: A large video database for human motion recognition. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’11). 2556--2563. Google ScholarDigital Library
Alina Kuznetsova, Sung Ju Hwang, Bodo Rosenhahn, and Leonid Sigal. 2016. Exploiting view-specific appearance similarities across classes for zero-shot pose prediction: A metric learning approach. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI’16). 3523--3529. Google ScholarDigital Library
Christoph H. Lampert, Hannes Nickisch, and Stefan Harmeling. 2009. Learning to detect unseen object classes by between-class attribute transfer. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’09). 951--958.Google ScholarCross Ref
Hugo Larochelle, Dumitru Erhan, and Yoshua Bengio. 2008. Zero-data learning of new tasks. In Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI’08). 646--651. Google ScholarDigital Library
Angeliki Lazaridou, Elia Bruni, and Marco Baroni. 2014. Is this a wampimuk? Cross-modal mapping between distributional semantics and the visual world. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL’14). 1403--1414.Google ScholarCross Ref
Angeliki Lazaridou, Georgiana Dinu, and Marco Baroni. 2015. 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 of the Asian Federation of Natural Language Processing (ACL’15). 270--280.Google Scholar
Chung-Wei Lee, Wei Fang, Chih-Kuan Yeh, and Yu-Chiang Frank Wang. 2018. Multi-label zero-shot learning with structured knowledge graphs. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18).Google ScholarCross Ref
Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer. 2017. Zero-shot relation extraction via reading comprehension. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL’17). 333--342.Google ScholarCross Ref
Hanhui Li, Donghui Li, and Xiaonan Luo. 2014. BAP: Bimodal attribute prediction for zero-shot image categorization. In Proceedings of the ACM International Conference on Multimedia (MM’14). 1013--1016. Google ScholarDigital Library
Xin Li and Yuhong Guo. 2015. Max-margin zero-shot learning for multi-class classification. In Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics (AISTATS’15). 626--634.Google Scholar
Xin Li, Yuhong Guo, and Dale Schuurmans. 2015. Semi-supervised zero-shot classification with label representation learning. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’15). 4211--4219. Google ScholarDigital Library
Xirong Li, Shuai Liao, Weiyu Lan, Xiaoyong Du, and Gang Yang. 2015. Zero-shot image tagging by hierarchical semantic embedding. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’15). 879--882. Google ScholarDigital Library
Yan Li, Zhen Jia, Junge Zhang, Kaiqi Huang, and Tieniu Tan. 2018. Deep semantic structural constraints for zero-shot learning. In Thirty-Second AAAI Conference on Artificial Intelligence (AAAI’18).Google ScholarCross Ref
Yanan Li, Donghui Wang, Huanhang Hu, Yuetan Lin, and Yueting Zhuang. 2017. Zero-shot recognition using dual visual-semantic mapping paths. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17). 5207--5215.Google ScholarCross Ref
Yan Li, Junge Zhang, Jianguo Zhang, and Kaiqi Huang. 2018. Discriminative learning of latent features for zero-shot recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18).Google ScholarCross Ref
Zhenyang Li, Efstratios Gavves, Thomas Mensink, and Cees G. M. Snoek. 2014. Attributes make sense on segmented objects. In European Conference on Computer Vision (ECCV’14). 350--365.Google Scholar
Zechao Li and Jinhui Tang. 2017. Weakly supervised deep matrix factorization for social image understanding. IEEE Transactions on Image Processing 26, 1 (2017), 276--288. Google ScholarDigital Library
Zechao Li, Jinhui Tang, and Xiaofei He. 2018. Robust structured nonnegative matrix factorization for image representation. IEEE Transactions on Neural Networks and Learning Systems 29, 5 (2018), 1947--1960.Google ScholarCross Ref
Kongming Liang, Hong Chang, Shiguang Shan, and Xilin Chen. 2015. A unified multiplicative framework for attribute learning. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’15). 2506--2514. Google ScholarDigital Library
Jingen Liu, Benjamin Kuipers, and Silvio Savarese. 2011. Recognizing human actions by attributes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’11). 3337--3344. Google ScholarDigital Library
Yang Long, Li Liu, Ling Shao, Fumin Shen, Guiguang Ding, and Jungong Han. 2017. From zero-shot learning to conventional supervised classification: Unseen visual data synthesis. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17). 6165--6174.Google ScholarCross Ref
Yukun Ma, Erik Cambria, and Sa Gao. 2016. Label embedding for zero-shot fine-grained named entity typing. In 26th International Conference on Computational Linguistics (COLING’16). 171--180.Google Scholar
Thomas Mensink, Efstratios Gavves, and Cees G. M. Snoek. 2014. Costa: Co-occurrence statistics for zero-shot classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’14). 2441--2448. Google ScholarDigital Library
Pascal Mettes and Cees G. M. Snoek. 2017. Spatial-aware object embeddings for zero-shot localization and classification of actions. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’17). 4453--4462.Google Scholar
Tomas Mikolov, Quoc V. Le, and Ilya Sutskever. 2013. Exploiting similarities among languages for machine translation. arXiv preprint arXiv:1309.4168 (2013).Google Scholar
Tanmoy Mukherjee and Timothy Hospedales. 2016. Gaussian visual-linguistic embedding for zero-shot recognition. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP’16). 912--918.Google ScholarCross Ref
Shujon Naha and Yang Wang. 2016. Object figure-ground segmentation using zero-shot learning. In 23rd International Conference on Pattern Recognition (ICPR’16). 2842--2847.Google ScholarCross Ref
Ndapandula Nakashole and Raphael Flauger. 2017. Knowledge distillation for bilingual dictionary induction. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP’17). 2497--2506.Google ScholarCross Ref
Hideki Nakayama and Noriki Nishida. 2017. Zero-resource machine translation by multimodal encoder-decoder network with multimedia pivot. Machine Translation 31, 1--2 (2017), 49--64. Google ScholarDigital Library
Jinseok Nam, Eneldo Loza Mencía, Hyunwoo J. Kim, and Johannes Fürnkranz. 2015. Predicting unseen labels using label hierarchies in large-scale multi-label learning. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD’15). 102--118. Google ScholarDigital Library
Maria-Elena Nilsback and Andrew Zisserman. 2008. Automated flower classification over a large number of classes. In Sixth Indian Conference on Computer Vision, Graphics & Image Processing (ICVGIP’08). 722--729. Google ScholarDigital Library
Li Niu, Ashok Veeraraghavan, and Ashutosh Sabharwal. 2018. Webly supervised learning meets zero-shot learning: A hybrid approach for fine-grained classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18).Google ScholarCross Ref
Mohammad Norouzi, Tomas Mikolov, Samy Bengio, Yoram Singer, Jonathon Shlens, Andrea Frome, Greg S. Corrado, and Jeffrey Dean. 2014. Zero-shot learning by convex combination of semantic embeddings. In International Conference on Learning Representations (ICLR’14).Google Scholar
Junhyuk Oh, Satinder Singh, Honglak Lee, and Pushmeet Kohli. 2017. Zero-shot task generalization with multi-task deep reinforcement learning. In Proceedings of the 34th International Conference on Machine Learning (ICML’17). 2661--2670. Google ScholarDigital Library
Paul Over, Jon Fiscus, Greg Sanders, David Joy, Martial Michel, George Awad, Alan F. Smeaton, Wessel Kraaij, and Georges Quénot. 2013. TRECVID 2013--An overview of the goals, tasks, data, evaluation mechanisms, and metrics. In TREC Video Retrieval Evaluation Workshop (TRECVID Workshop’13). https://wwwnlpir.nist.gov/projects/tvpubs/tv13.papers/tv13overview.pdf.Google Scholar
Mark Palatucci, Dean Pomerleau, Geoffrey Hinton, and Tom M. Mitchell. 2009. Zero-shot learning with semantic output codes. In Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009 (NIPS’09). 1410--1418. Google ScholarDigital Library
Sinno Jialin Pan. 2014. Transfer learning. In Data Classification: Algorithms and Applications. 537--570.Google Scholar
Sinno Jialin Pan and Qiang Yang. 2010. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22, 10 (2010), 1345--1359. Google ScholarDigital Library
Devi Parikh and Kristen Grauman. 2011. Relative attributes. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’11). 503--510. Google ScholarDigital Library
Panupong Pasupat and Percy Liang. 2014. Zero-shot entity extraction from web pages. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL’14). 391--401.Google ScholarCross Ref
Genevieve Patterson and James Hays. 2012. SUN attribute database: Discovering, annotating, and recognizing scene attributes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’12). 2751--2758. Google ScholarDigital Library
Te Pi, Xi Li, and Zhongfei (Mark) Zhang. 2017. Boosted zero-shot learning with semantic correlation regularization. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI’17). 2599--2605. Google ScholarDigital Library
Ruizhi Qiao, Lingqiao Liu, Chunhua Shen, and Anton van den Hengel. 2016. Less is more: Zero-shot learning from online textual documents with noise suppression. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). 2249--2257.Google Scholar
Jie Qin, Li Liu, Ling Shao, Fumin Shen, Bingbing Ni, Jiaxin Chen, and Yunhong Wang. 2017. Zero-shot action recognition with error-correcting output codes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17). 1042--1051.Google ScholarCross Ref
Sachin Ravi and Hugo Larochelle. 2017. Optimization as a model for few-shot learning. In International Conference on Learning Representations (ICLR’17).Google Scholar
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H. Lampert. 2017. iCaRL: Incremental classifier and representation learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17). 5533--5542.Google Scholar
Scott Reed, Zeynep Akata, Honglak Lee, and Bernt Schiele. 2016. Learning deep representations of fine-grained visual descriptions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). 49--58.Google ScholarCross Ref
Zhou Ren, Hailin Jin, Zhe Lin, Chen Fang, and Alan Yuille. 2016. Joint image-text representation by Gaussian visual-semantic embedding. In Proceedings of the 2016 ACM Conference on Multimedia Conference (MM’16). 207--211. Google ScholarDigital Library
Zhou Ren, Hailin Jin, Zhe Lin, Chen Fang, and Alan Yuille. 2017. Multiple instance visual-semantic embedding. In British Machine Vision Conference (BMVC’17).Google ScholarCross Ref
Pieter Robyns, Eduard Marin, Wim Lamotte, Peter Quax, Dave Singelée, and Bart Preneel. 2017. 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 (WiSec’17). 58--63. Google ScholarDigital Library
Marcus Rohrbach, Sandra Ebert, and Bernt Schiele. 2013. Transfer learning in a transductive setting. In Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems (NIPS’13). 46--54. Google ScholarDigital Library
Marcus Rohrbach, Michael Stark, and Bernt Schiele. 2011. Evaluating knowledge transfer and zero-shot learning in a large-scale setting. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’11). 1641--1648. Google ScholarDigital Library
Marcus Rohrbach, Michael Stark, György Szarvas, Iryna Gurevych, and Bernt Schiele. 2010. What helps where - and why? Semantic relatedness for knowledge transfer. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’10). 910--917.Google ScholarCross Ref
Bernardino Romera-Paredes and Philip H. S. Torr. 2015. An embarrassingly simple approach to zero-shot learning. In Proceedings of the 32nd International Conference on Machine Learning (ICML’15). 2152--2161. Google ScholarDigital Library
Walter J. Scheirer, Anderson de Rezende Rocha, Archana Sapkota, and Terrance E. Boult. 2013. Toward open set recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 7 (2013), 1757--1772. Google ScholarDigital Library
Yuming Shen, Li Liu, Fumin Shen, and Ling Shao. 2018. Zero-shot sketch-image hashing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18).Google ScholarCross Ref
Yutaro Shigeto, Ikumi Suzuki, Kazuo Hara, Masashi Shimbo, and Yuji Matsumoto. 2015. Ridge regression, hubness, and zero-shot learning. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD’15). 135--151. Google ScholarDigital Library
Richard Socher, Milind Ganjoo, Christopher D. Manning, and Andrew Y. Ng. 2013. Zero-shot learning through cross-modal transfer. In Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems (NIPS’13). 935--943. Google ScholarDigital Library
Fengyi Song and Yi Chen. 2016. A survey on zero-shot learning. Journal of Science 5, 1 (2016), 455--460.Google Scholar
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. 2012. UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402 (2012).Google Scholar
Yao-Hung Hubert Tsai, Liang-Kang Huang, and Ruslan Salakhutdinov. 2017. Learning robust visual-semantic embeddings. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’17). 3591--3600.Google Scholar
Naman Turakhia and Devi Parikh. 2013. Attribute dominance: What pops out? In Proceedings of the IEEE International Conference on Computer Vision (ICCV’13). 1225--1232. Google ScholarDigital Library
Subhashini Venugopalan, Lisa Anne Hendricks, Marcus Rohrbach, Raymond Mooney, Trevor Darrell, and Kate Saenko. 2017. Captioning images with diverse objects. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17). 1170--1178.Google ScholarCross Ref
Vinay Kumar Verma, Gundeep Arora, Ashish Mishra, and Piyush Rai. 2018. Generalized zero-shot learning via synthesized examples. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18).Google ScholarCross Ref
Vinay Kumar Verma and Piyush Rai. 2017. A simple exponential family framework for zero-shot learning. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD’17). 792--808.Google ScholarCross Ref
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. 2011. The Caltech-UCSD Birds-200-2011 Dataset. Technical Report CNS-TR-2011-001. California Institute of Technology.Google Scholar
Donghui Wang, Yanan Li, Yuetan Lin Lin, and Yueting Zhuang. 2016. Relational knowledge transfer for zero-shot learning. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI’16). 2145--2151. Google ScholarDigital Library
Gang Wang, David Forsyth, and Derek Hoiem. 2010. Comparative object similarity for improved recognition with few or no examples. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’10). 3525--3532.Google ScholarCross Ref
Qian Wang and Ke Chen. 2017. Alternative semantic representations for zero-shot human action recognition. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD’17). 87--102.Google ScholarCross Ref
Qian Wang and Ke Chen. 2017. Zero-shot visual recognition via bidirectional latent embedding. International Journal of Computer Vision 124, 3 (2017), 356--383. Google ScholarDigital Library
Wei Wang, Chunyan Miao, and Shuji Hao. 2017. Zero-shot human activity recognition via nonlinear compatibility based method. In Proceedings of the International Conference on Web Intelligence (WI’17). 322--330. Google ScholarDigital Library
Xiaoyang Wang and Qiang Ji. 2013. A unified probabilistic approach modeling relationships between attributes and objects. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’13). 2120--2127. Google ScholarDigital Library
Xiaolong Wang, Yufei Ye, and Abhinav Gupta. 2018. Zero-shot recognition via semantic embeddings and knowledge graphs. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18).Google ScholarCross Ref
Zheng Wang, Ruimin Hu, Chao Liang, Yi Yu, Junjun Jiang, Mang Ye, Jun Chen, and Qingming Leng. 2016. Zero-shot person re-identification via cross-view consistency. IEEE Transactions on Multimedia 18, 2 (2016), 260--272.Google ScholarDigital Library
Karl Weiss, Taghi M. Khoshgoftaar, and DingDing Wang. 2016. A survey of transfer learning. Journal of Big Data 3, 1 (2016), 9.Google ScholarCross Ref
Shuang Wu, Sravanthi Bondugula, Florian Luisier, Xiaodan Zhuang, and Pradeep Natarajan. 2014. Zero-shot event detection using multi-modal fusion of weakly supervised concepts. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’14). 2665--2672. Google ScholarDigital Library
Yongqin Xian, Zeynep Akata, Gaurav Sharma, Quynh Nguyen, Matthias Hein, and Bernt Schiele. 2016. Latent embeddings for zero-shot classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). 69--77.Google ScholarCross Ref
Yongqin Xian, Christoph H. Lampert, Bernt Schiele, and Zeynep Akata. 2017. Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly. arXiv preprint arXiv:1707.00600v3 (2017).Google Scholar
Yongqin Xian, Tobias Lorenz, Bernt Schiele, and Zeynep Akata. 2018. Feature generating networks for zero-shot learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18).Google ScholarCross Ref
Ruobing Xie, Zhiyuan Liu, Jia Jia, Huanbo Luan, and Maosong Sun. 2016. Representation learning of knowledge graphs with entity descriptions. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI’16). 2659--2665. Google ScholarDigital Library
Sihong Xie, Shaoxiong Wang, and Philip S. Yu. 2016. Active zero-shot learning. In Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM’16). 1889--1892. Google ScholarDigital Library
Sihong Xie and Philip S. Yu. 2017. Active zero-shot learning: A novel approach to extreme multi-labeled classification. International Journal of Data Science and Analytics 3, 3 (2017), 151--160.Google ScholarCross Ref
Baohan Xu, Yanwei Fu, Yu-Gang Jiang, Boyang Li, and Leonid Sigal. 2016. Video emotion recognition with transferred deep feature encodings. In Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval (ICMR’16). 15--22. Google ScholarDigital Library
Xun Xu, Timothy Hospedales, and Shaogang Gong. 2017. Transductive zero-shot action recognition by word-vector embedding. International Journal of Computer Vision 123, 3 (2017), 309--333. Google ScholarDigital Library
Xun Xu, Timothy M. Hospedales, and Shaogang Gong. 2016. Multi-task zero-shot action recognition with prioritised data augmentation. In European Conference on Computer Vision (ECCV’16). 343--359.Google ScholarCross Ref
Xing Xu, Fumin Shen, Yang Yang, Jie Shao, and Zi Huang. 2017. Transductive visual-semantic embedding for zero-shot learning. In Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval (ICMR’17). 41--49. Google ScholarDigital Library
Xing Xu, Fumin Shen, Yang Yang, Dongxiang Zhang, Heng Tao Shen, and Jingkuan Song. 2017. Matrix tri-factorization with manifold regularizations for zero-shot learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17). 2007--2016.Google ScholarCross Ref
Yongxin Yang and Timothy M. Hospedales. 2015. A unified perspective on multi-domain and multi-task learning. In International Conference on Learning Representations (ICLR’15).Google Scholar
Yongxin Yang and Timothy M. Hospedales. 2016. Multivariate regression on the Grassmannian for predicting novel domains. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). 5071--5080.Google Scholar
Yang Yang, Yadan Luo, Weilun Chen, Fumin Shen, Jie Shao, and Heng Tao Shen. 2016. Zero-shot hashing via transferring supervised knowledge. In Proceedings of the 2016 ACM Conference on Multimedia Conference (MM’16). 1286--1295. Google ScholarDigital Library
Majid Yazdani and James Henderson. 2015. A model of zero-shot learning of spoken language understanding. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP’15). 244--249.Google ScholarCross Ref
Meng Ye and Yuhong Guo. 2017. Zero-shot classification with discriminative semantic representation learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17). 5103--5111.Google ScholarCross Ref
Felix X. Yu, Liangliang Cao, Rogerio S. Feris, John R. Smith, and Shih-Fu Chang. 2013. Designing category-level attributes for discriminative visual recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’13). 771--778. Google ScholarDigital Library
Li Zhang, Tao Xiang, and Shaogang Gong. 2017. Learning a deep embedding model for zero-shot learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17). 3010--3019.Google ScholarCross Ref
Yang Zhang, Boqing Gong, and Mubarak Shah. 2016. Fast zero-shot image tagging. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). 5985--5994.Google ScholarCross Ref
Ziming Zhang and Venkatesh Saligrama. 2015. Zero-shot learning via semantic similarity embedding. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’15). 4166--4174. Google ScholarDigital Library
Ziming Zhang and Venkatesh Saligrama. 2016. Zero-shot learning via joint latent similarity embedding. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). 6034--6042.Google ScholarCross Ref
Ziming Zhang and Venkatesh Saligrama. 2016. Zero-shot recognition via structured prediction. In European Conference on Computer Vision (ECCV’16). 533--548.Google ScholarCross Ref
Bo Zhao, Botong Wu, Tianfu Wu, and Yizhou Wang. 2017. Zero-shot learning posed as a missing data problem. In IEEE International Conference on Computer Vision Workshops: Transferring and Adapting Source Knowledge in Computer Vision (ICCV Workshops’17). 2616--2622.Google ScholarCross Ref
Hao Zheng, Yong Cheng, and Yang Liu. 2017. Maximum expected likelihood estimation for zero-resource neural machine translation. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI’17). 4251--4257. Google ScholarDigital Library
Vincent W. Zheng, Derek Hao Hu, and Qiang Yang. 2009. Cross-domain activity recognition. In Proceedings of the 11th International Conference on Ubiquitous Computing (UbiComp’09). 61--70. Google ScholarDigital Library
Yizhe Zhu, Mohamed Elhoseiny, Bingchen Liu, Xi Peng, and Ahmed Elgammal. 2018. A generative adversarial approach for zero-shot learning from noisy texts. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18).Google ScholarCross Ref

Index Terms

A Survey of Zero-Shot Learning: Settings, Methods, and Applications
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Multi-task learning
        Transfer learning

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Published in
ACM Transactions on Intelligent Systems and Technology Volume 10, Issue 2
Survey Papers and Regular Papers
March 2019
214 pages
ISSN:2157-6904
EISSN:2157-6912
DOI:10.1145/3306498
Editor:
Yu Zheng
JD Finance, China
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Publication History
- Published: 16 January 2019
- Revised: 1 September 2018
- Accepted: 1 September 2018
- Received: 1 February 2018
Published in tist Volume 10, Issue 2

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A Survey of Zero-Shot Learning: Settings, Methods, and Applications

ACM Transactions on Intelligent Systems and Technology

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Ordinal zero-shot learning

Zero-Shot Learning with Noisy Labels

Distributed representation of tags for Active Zero Shot learning

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