A Review of Generalized Zero-Shot Learning Methods

Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples under the condition that some output classes are unknown during supervised learning. To address this challenging task, GZSL leverages semantic information of both seen (source) and unseen (target) classes to bridge the gap between both seen and unseen classes. Since its introduction, many GZSL models have been formulated. In this review paper, we present a comprehensive review of GZSL. Firstly, we provide an overview of GZSL including the problems and challenging issues. Then, we introduce a hierarchical categorization of the GZSL methods and discuss the representative methods of each category. In addition, we discuss several research directions for future studies.

[1]  Wojciech Zaremba,et al.  Improved Techniques for Training GANs , 2016, NIPS.

[2]  Xiaogang Wang,et al.  DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Qionghai Dai,et al.  Dual-View Ranking with Hardness Assessment for Zero-Shot Learning , 2019, AAAI.

[4]  Soma Biswas,et al.  Preserving Semantic Relations for Zero-Shot Learning , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[5]  Fei Zhang,et al.  Co-Representation Network for Generalized Zero-Shot Learning , 2019, ICML.

[6]  Liang Wang,et al.  Deep Unbiased Embedding Transfer for Zero-Shot Learning , 2020, IEEE Transactions on Image Processing.

[7]  Chris H. Q. Ding,et al.  Orthogonal nonnegative matrix t-factorizations for clustering , 2006, KDD '06.

[8]  Xiufen Ye,et al.  Generalized Zero Shot Learning via Synthesis Pseudo Features , 2019, IEEE Access.

[9]  Christoph H. Lampert,et al.  iCaRL: Incremental Classifier and Representation Learning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Zhongfei Zhang,et al.  Zero-Shot Learning via Latent Space Encoding , 2017, IEEE Transactions on Cybernetics.

[11]  Bernt Schiele,et al.  Feature Generating Networks for Zero-Shot Learning , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[12]  Heng Tao Shen,et al.  Ocean: A Dual Learning Approach For Generalized Zero-Shot Sketch-Based Image Retrieval , 2020, 2020 IEEE International Conference on Multimedia and Expo (ICME).

[13]  Narayanan C. Krishnan,et al.  Semantically Aligned Bias Reducing Zero Shot Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Pascal Vincent,et al.  Generalized Denoising Auto-Encoders as Generative Models , 2013, NIPS.

[15]  Yuxuan Luo,et al.  A novel dataset-specific feature extractor for zero-shot learning , 2020, Neurocomputing.

[16]  Jure Leskovec,et al.  node2vec: Scalable Feature Learning for Networks , 2016, KDD.

[17]  Ling Shao,et al.  A Joint Label Space for Generalized Zero-Shot Classification , 2020, IEEE Transactions on Image Processing.

[18]  Philip H. S. Torr,et al.  An embarrassingly simple approach to zero-shot learning , 2015, ICML.

[19]  Georgiana Dinu,et al.  Improving zero-shot learning by mitigating the hubness problem , 2014, ICLR.

[20]  Pietro Perona,et al.  Caltech-UCSD Birds 200 , 2010 .

[21]  Navdeep Jaitly,et al.  Adversarial Autoencoders , 2015, ArXiv.

[22]  Li Liu,et al.  A Joint Generative Model for Zero-Shot Learning , 2018, ECCV Workshops.

[23]  Yang Liu,et al.  Transductive Unbiased Embedding for Zero-Shot Learning , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[24]  Léon Bottou,et al.  Wasserstein Generative Adversarial Networks , 2017, ICML.

[25]  Shiguang Shan,et al.  Learning Class Prototypes via Structure Alignment for Zero-Shot Recognition , 2018, ECCV.

[26]  Jonathon Shlens,et al.  Conditional Image Synthesis with Auxiliary Classifier GANs , 2016, ICML.

[27]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  Michel Crucianu,et al.  Modeling Inter and Intra-Class Relations in the Triplet Loss for Zero-Shot Learning , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[29]  Wei-Lun Chao,et al.  An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild , 2016, ECCV.

[30]  Heng Tao Shen,et al.  Pseudo Transfer with Marginalized Corrupted Attribute for Zero-shot Learning , 2018, ACM Multimedia.

[31]  Debasmit Das,et al.  Zero-shot Image Recognition Using Relational Matching, Adaptation and Calibration , 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

[32]  Bernt Schiele,et al.  Attribute Prototype Network for Zero-Shot Learning , 2020, NeurIPS.

[33]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[34]  Bernt Schiele,et al.  Zero-Shot Learning — The Good, the Bad and the Ugly , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Zi Huang,et al.  Leveraging the Invariant Side of Generative Zero-Shot Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[36]  Jian Ni,et al.  Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning , 2019, NeurIPS.

[37]  Michel Crucianu,et al.  From Classical to Generalized Zero-Shot Learning: a Simple Adaptation Process , 2018, MMM.

[38]  Guihua Wen,et al.  Grouping attributes zero-shot learning for tongue constitution recognition , 2020, Artif. Intell. Medicine.

[39]  Ling Shao,et al.  Zero-Shot Learning Using Synthesised Unseen Visual Data with Diffusion Regularisation , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[41]  Zhenfeng Zhu,et al.  From Anchor Generation to Distribution Alignment: Learning a Discriminative Embedding Space for Zero-Shot Recognition , 2020, ArXiv.

[42]  Jianfei Cai,et al.  Zero-Shot Learning via Category-Specific Visual-Semantic Mapping and Label Refinement , 2019, IEEE Transactions on Image Processing.

[43]  XiangTao,et al.  Transductive Multi-View Zero-Shot Learning , 2015 .

[44]  Rob Fergus,et al.  Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks , 2015, NIPS.

[45]  Abhinav Gupta,et al.  Zero-Shot Recognition via Semantic Embeddings and Knowledge Graphs , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[46]  Guoyin Wang,et al.  An Optimal Transport Framework for Zero-Shot Learning , 2019, ArXiv.

[47]  Geoffrey E. Hinton,et al.  Distilling the Knowledge in a Neural Network , 2015, ArXiv.

[48]  Philip S. Yu,et al.  Generative Dual Adversarial Network for Generalized Zero-Shot Learning , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[49]  Joost van de Weijer,et al.  Simple and effective localized attribute representations for zero-shot learning , 2020, ArXiv.

[50]  Songcan Chen,et al.  Guided CNN for generalized zero-shot and open-set recognition using visual and semantic prototypes , 2020, Pattern Recognit..

[51]  Pietro Perona,et al.  One-shot learning of object categories , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[52]  Rafael Felix,et al.  Generalised Zero-Shot Learning with Domain Classification in a Joint Semantic and Visual Space , 2019, 2019 Digital Image Computing: Techniques and Applications (DICTA).

[53]  Jiechao Guan,et al.  Extreme Reverse Projection Learning for Zero-Shot Recognition , 2018, ACCV.

[54]  Ling Shao,et al.  Zero Shot Learning via Low-rank Embedded Semantic AutoEncoder , 2018, IJCAI.

[55]  Wei Liu,et al.  Zero-Shot Visual Recognition Using Semantics-Preserving Adversarial Embedding Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[56]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[57]  Andrew Y. Ng,et al.  Zero-Shot Learning Through Cross-Modal Transfer , 2013, NIPS.

[58]  James Hays,et al.  SUN attribute database: Discovering, annotating, and recognizing scene attributes , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[59]  Lars Petersson,et al.  Mitigating the Hubness Problem for Zero-Shot Learning of 3D Objects , 2019, BMVC.

[60]  Xinhang Song,et al.  Generalized Zero-shot Learning with Multi-source Semantic Embeddings for Scene Recognition , 2020, ACM Multimedia.

[61]  Mahdi Rezaei,et al.  Zero-shot learning and its applications from autonomous vehicles to COVID-19 diagnosis: A review , 2020, Intelligence-Based Medicine.

[62]  Chee Peng Lim,et al.  A Q-learning-based multi-agent system for data classification , 2017, Appl. Soft Comput..

[63]  Lars Petersson,et al.  Transductive Zero-Shot Learning for 3D Point Cloud Classification , 2019, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

[64]  Krista A. Ehinger,et al.  SUN database: Large-scale scene recognition from abbey to zoo , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[65]  Jihong Guan,et al.  Simple Is Better: A Global Semantic Consistency Based End-to-End Framework for Effective Zero-Shot Learning , 2019, PRICAI.

[66]  Nanning Zheng,et al.  Compressing Unknown Images With Product Quantizer for Efficient Zero-Shot Classification , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[67]  Yu-Chiang Frank Wang,et al.  Multi-label Zero-Shot Learning with Structured Knowledge Graphs , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[68]  Ling Shao,et al.  Zero-VAE-GAN: Generating Unseen Features for Generalized and Transductive Zero-Shot Learning , 2020, IEEE Transactions on Image Processing.

[69]  Shaogang Gong,et al.  Recent Advances in Zero-Shot Recognition: Toward Data-Efficient Understanding of Visual Content , 2018, IEEE Signal Processing Magazine.

[70]  Piyush Rai,et al.  Meta-Learning for Generalized Zero-Shot Learning , 2020, AAAI.

[71]  Pengtao Xie,et al.  Generalized Zero-Shot Text Classification for ICD Coding , 2020, IJCAI.

[72]  Gustavo Carneiro,et al.  Multi-modal Cycle-consistent Generalized Zero-Shot Learning , 2018, ECCV.

[73]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[74]  Zihao Wang,et al.  Joint Projection and Subspace Learning for Zero-Shot Recognition , 2019, 2019 IEEE International Conference on Multimedia and Expo (ICME).

[75]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[76]  拓海 杉山,et al.  “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .

[77]  I. Biederman.  Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.

[78]  Zhongfei Zhang,et al.  A Semantics-Guided Class Imbalance Learning Model for Zero-Shot Classification , 2019, ArXiv.

[79]  Chee Peng Lim,et al.  An improved fuzzy ARTMAP and Q-learning agent model for pattern classification , 2019, Neurocomputing.

[80]  Yanan Li,et al.  Zero-Shot Recognition Using Dual Visual-Semantic Mapping Paths , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[81]  Mohamed Elhoseiny,et al.  Creativity Inspired Zero-Shot Learning , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[82]  Zhiwu Lu,et al.  Zero-Shot Learning with Superclasses , 2018, ICONIP.

[83]  Alexei A. Efros,et al.  Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[84]  Erik Cambria,et al.  ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis , 2021, Future Gener. Comput. Syst..

[85]  Ali Farhadi,et al.  Describing objects by their attributes , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[86]  Kaizhu Huang,et al.  Discriminant Zero-Shot Learning with Center Loss , 2019, Cognitive Computation.

[87]  Min Fang,et al.  Learning domain invariant unseen features for generalized zero-shot classification , 2020, Knowl. Based Syst..

[88]  Soma Biswas,et al.  Generative Model with Semantic Embedding and Integrated Classifier for Generalized Zero-Shot Learning , 2020, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

[89]  Christoph H. Lampert,et al.  Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[90]  Lu Liu,et al.  Attribute Propagation Network for Graph Zero-Shot Learning , 2020, AAAI.

[91]  Wei Wei,et al.  Discriminative Embedding Autoencoder With a Regressor Feedback for Zero-Shot Learning , 2019, IEEE Access.

[92]  Ole Winther,et al.  Autoencoding beyond pixels using a learned similarity metric , 2015, ICML.

[93]  Chen Xu,et al.  The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding , 2014, International Journal of Computer Vision.

[94]  Ling Shao,et al.  Label-activating framework for zero-shot learning , 2020, Neural Networks.

[95]  Guangqing Chi,et al.  Energy choices in Alaska: Mining people's perception and attitudes from geotagged tweets , 2020 .

[96]  Ling Shao,et al.  Triple Verification Network for Generalized Zero-Shot Learning , 2019, IEEE Transactions on Image Processing.

[97]  Shaogang Gong,et al.  Semantic Autoencoder for Zero-Shot Learning , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[98]  Matthieu Cord,et al.  Zero-Shot Semantic Segmentation , 2019, NeurIPS.

[99]  Fei-Fei Li,et al.  Novel Dataset for Fine-Grained Image Categorization : Stanford Dogs , 2012 .

[100]  Bo Zhao,et al.  Zero-shot Learning via Shared-Reconstruction-Graph Pursuit , 2017, ArXiv.

[101]  Haofeng Zhang,et al.  Asymmetric graph based zero shot learning , 2020, Multimedia Tools and Applications.

[102]  Zi Huang,et al.  Alleviating Feature Confusion for Generative Zero-shot Learning , 2019, ACM Multimedia.

[103]  Christoph H. Lampert,et al.  Learning to detect unseen object classes by between-class attribute transfer , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[104]  Yuejun Li,et al.  Inductive Zero-Shot Image Annotation via Embedding Graph , 2019, IEEE Access.

[105]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[106]  Andrew L. Maas.  Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .

[107]  Tao Xiang,et al.  Learning to Compare: Relation Network for Few-Shot Learning , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[108]  Gal Chechik,et al.  Adaptive Confidence Smoothing for Generalized Zero-Shot Learning , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[109]  Fatih Porikli,et al.  A Unified Approach for Conventional Zero-Shot, Generalized Zero-Shot, and Few-Shot Learning , 2017, IEEE Transactions on Image Processing.

[110]  Tao Xiang,et al.  Learning a Deep Embedding Model for Zero-Shot Learning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[111]  Ling Shao,et al.  Deep transductive network for generalized zero shot learning , 2020, Pattern Recognit..

[112]  Ling Shao,et al.  Dual triplet network for image zero-shot learning , 2020, Neurocomputing.

[113]  Dat T. Huynh,et al.  Fine-Grained Generalized Zero-Shot Learning via Dense Attribute-Based Attention , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[114]  Tao Shen,et al.  DiSAN: Directional Self-Attention Network for RNN/CNN-free Language Understanding , 2017, AAAI.

[115]  Hema A. Murthy,et al.  A Generative Model for Zero Shot Learning Using Conditional Variational Autoencoders , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[116]  Yuji Matsumoto,et al.  Ridge Regression, Hubness, and Zero-Shot Learning , 2015, ECML/PKDD.

[117]  Mei-Chen Yeh,et al.  Generalized Zero-Shot Recognition Through Image-Guided Semantic Classification , 2020, 2021 IEEE International Conference on Image Processing (ICIP).

[118]  Frédéric Jurie,et al.  Generating Visual Representations for Zero-Shot Classification , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

[119]  Vinay P. Namboodiri,et al.  AVGZSLNet: Audio-Visual Generalized Zero-Shot Learning by Reconstructing Label Features from Multi-Modal Embeddings , 2020, 2021 IEEE Winter Conference on Applications of Computer Vision (WACV).

[120]  Bernt Schiele,et al.  F-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[121]  Ling Shao,et al.  Learning discriminative domain-invariant prototypes for generalized zero shot learning , 2020, Knowl. Based Syst..

[122]  Ke Chen,et al.  Multi-label zero-shot human action recognition via joint latent ranking embedding , 2017, Neural Networks.

[123]  Ling Shao,et al.  Out-Of-Distribution Detection for Generalized Zero-Shot Action Recognition , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[124]  Tao Xiang,et al.  Zero-Shot Learning on Semantic Class Prototype Graph , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[125]  Alexandros Nanopoulos,et al.  Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data , 2010, J. Mach. Learn. Res..

[126]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[127]  Trevor Darrell,et al.  Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[128]  Aaron C. Courville,et al.  Improved Training of Wasserstein GANs , 2017, NIPS.

[129]  Xiaorui Li,et al.  Generalized Zero-Shot Learning With Multi-Channel Gaussian Mixture VAE , 2020, IEEE Signal Processing Letters.

[130]  Gustavo Carneiro,et al.  Multi-modal Ensemble Classification for Generalized Zero Shot Learning , 2019, ArXiv.

[131]  Eduardo F. Morales,et al.  An Introduction to Reinforcement Learning , 2011 .

[132]  Saeid Nahavandi,et al.  SpinalNet: Deep Neural Network with Gradual Input , 2020, ArXiv.

[133]  Yiguang Liu,et al.  Improving Generalized Zero-Shot Learning by Semantic Discriminator , 2020, ArXiv.

[134]  Wei-Lun Chao,et al.  Classifier and Exemplar Synthesis for Zero-Shot Learning , 2018, International Journal of Computer Vision.

[135]  Samy Bengio,et al.  Zero-Shot Learning by Convex Combination of Semantic Embeddings , 2013, ICLR.

[136]  Xirong Li,et al.  Dissimilarity Representation Learning for Generalized Zero-Shot Recognition , 2018, ACM Multimedia.

[137]  Soma Biswas,et al.  Generalized Zero-Shot Cross-Modal Retrieval , 2019, IEEE Transactions on Image Processing.

[138]  Yang Yang,et al.  CANZSL: Cycle-Consistent Adversarial Networks for Zero-Shot Learning from Natural Language , 2019, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

[139]  Herna L. Viktor,et al.  Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach , 2004, SKDD.

[140]  S. Fienberg,et al.  The Clockwork Muse: The Predictability of Artistic Change. , 1991 .

[141]  Heng Tao Shen,et al.  Similarity preserving feature generating networks for zero-shot learning , 2020, Neurocomputing.

[142]  Honglak Lee,et al.  Learning Structured Output Representation using Deep Conditional Generative Models , 2015, NIPS.

[143]  Guangfeng Lin,et al.  Transfer Feature Generating Networks With Semantic Classes Structure for Zero-Shot Learning , 2019, IEEE Access.

[144]  Lei Zhang,et al.  Towards Effective Deep Embedding for Zero-Shot Learning , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[145]  Venkatesh Saligrama,et al.  Don’t Even Look Once: Synthesizing Features for Zero-Shot Detection , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[146]  Kaizhu Huang,et al.  Self-focus Deep Embedding Model for Coarse-Grained Zero-Shot Classification , 2019, BICS.

[147]  Venkatesh Saligrama,et al.  Generalized Zero-Shot Recognition Based on Visually Semantic Embedding , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[148]  Shafin Rahman,et al.  Transductive Learning for Zero-Shot Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[149]  Li Liu,et al.  A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges , 2020, Inf. Fusion.

[150]  Anderson Rocha,et al.  Toward Open Set Recognition , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[151]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[152]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[153]  Michael I. Jordan,et al.  Generalized Zero-Shot Learning with Deep Calibration Network , 2018, NeurIPS.

[154]  Christoph H. Lampert,et al.  Attribute-Based Classification for Zero-Shot Visual Object Categorization , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[155]  Tanaya Guha,et al.  Coordinated Joint Multimodal Embeddings for Generalized Audio-Visual Zero-shot Classification and Retrieval of Videos , 2019, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

[156]  Xiaobo Jin,et al.  Attentive Region Embedding Network for Zero-Shot Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[157]  Laure Soulier,et al.  Context-Aware Zero-Shot Learning for Object Recognition , 2019, ICML.

[158]  Yang Yang,et al.  Matrix Tri-Factorization with Manifold Regularizations for Zero-Shot Learning , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[159]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[160]  Dat T. Huynh,et al.  A Shared Multi-Attention Framework for Multi-Label Zero-Shot Learning , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[161]  Zhong Ji,et al.  Multi-Modality Adversarial Auto-Encoder for Zero-Shot Learning , 2020, IEEE Access.

[162]  Piyush Rai,et al.  Generalized Zero-Shot Learning via Synthesized Examples , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[163]  U. Rajendra Acharya,et al.  A novel method for sentiment classification of drug reviews using fusion of deep and machine learning techniques , 2020, Knowl. Based Syst..

[164]  Devraj Mandal,et al.  Autoencoder based novelty detection for generalized zero shot learning , 2019, 2019 IEEE International Conference on Image Processing (ICIP).

[165]  Kibok Lee,et al.  Hierarchical Novelty Detection for Visual Object Recognition , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[166]  Bernt Schiele,et al.  Multi-cue Zero-Shot Learning with Strong Supervision , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[167]  Farhad Pourpanah,et al.  Recent advances in deep learning , 2020, International Journal of Machine Learning and Cybernetics.

[168]  Wei-Lun Chao,et al.  Synthesized Classifiers for Zero-Shot Learning , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[169]  Jihua Zhu,et al.  Domain segmentation and adjustment for generalized zero-shot learning , 2020, ArXiv.

[170]  Richard S. Zemel,et al.  Generative Moment Matching Networks , 2015, ICML.

[171]  Pietro Perona,et al.  The Caltech-UCSD Birds-200-2011 Dataset , 2011 .

[172]  Chunyan Miao,et al.  A Survey of Zero-Shot Learning , 2019, ACM Trans. Intell. Syst. Technol..

[173]  Jinchang Ren,et al.  SR-GAN: Semantic Rectifying Generative Adversarial Network for Zero-shot Learning , 2019, 2019 IEEE International Conference on Multimedia and Expo (ICME).

[174]  Oleksandr Makeyev,et al.  Neural network with ensembles , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[175]  Haofeng Zhang,et al.  Pseudo distribution on unseen classes for generalized zero shot learning , 2020, Pattern Recognit. Lett..

[176]  Maunil Rohitbhai Vyas.  Zero Shot Learning for Visual Object Recognition with Generative Models , 2020 .

[177]  Andrew Zisserman,et al.  Automated Flower Classification over a Large Number of Classes , 2008, 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing.

[178]  Jungong Han,et al.  Class-specific synthesized dictionary model for Zero-Shot Learning , 2019, Neurocomputing.

[179]  Pietro Perona,et al.  Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[180]  Wu Liu,et al.  Generalized zero-shot learning for action recognition with web-scale video data , 2017, World Wide Web.

[181]  Ahmed M. Elgammal,et al.  Link the Head to the "Beak": Zero Shot Learning from Noisy Text Description at Part Precision , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[182]  Yoshua Bengio,et al.  Zero-data Learning of New Tasks , 2008, AAAI.

[183]  Kaizhu Huang,et al.  Beyond Attributes: High-Order Attribute Features for Zero-Shot Learning , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

[184]  Yue Gao,et al.  SitNet: Discrete Similarity Transfer Network for Zero-shot Hashing , 2017, IJCAI.

[185]  Xi Peng,et al.  A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[186]  Chunhui Zhao,et al.  Transfer Increment for Generalized Zero-Shot Learning , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[187]  Anshumali Shrivastava,et al.  Semantic Similarity Based Softmax Classifier for Zero-Shot Learning , 2019, ArXiv.

[188]  Shiguang Shan,et al.  Transferable Contrastive Network for Generalized Zero-Shot Learning , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[189]  Zhongfei Zhang,et al.  Bi-Adversarial Auto-Encoder for Zero-Shot Learning , 2018, ArXiv.