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Zero-shot learning relies on semantic class representations such as hand-engineered attributes or learned embeddings to predict classes without any labeled examples.
Conceptnet—a practical commonsense reasoning tool-kit
Hugo Liu and Push Singh · 2004
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Bbn pronoun coreference and entity type corpus, 2005
Ralph Weischedel and Ada Brunstein · 2005
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Importance of semantic representation: Dataless classification
Ming-Wei Chang, Lev-Arie Ratinov, Dan Roth, and Vivek Srikumar · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Describing objects by their attributes
Ali Farhadi, Ian Endres, Derek Hoiem, and David A. Forsyth · 2009
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Torchvision: The machine-vision package of torch
Sébastien Marcel and Yann Rodriguez · 2010
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge J. Belongie · 2011
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Fine-grained entity recognition
Xiao Ling and Daniel S Weld · 2012
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Devise: A deep visual-semantic embedding model
Andrea Frome, Greg S Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Marc’Aurelio Ranzato, and Tomas Mikolov · 2013
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Attribute-based classification for zero-shot visual object categorization
Christoph H Lampert, Hannes Nickisch, and Stefan Harmeling · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Zero-shot learning through cross-modal transfer
Richard Socher, Milind Ganjoo, Christopher D Manning, and Andrew Ng · 2013
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A joint model for entity analysis: Coreference, typing, and linking
Greg Durrett and Dan Klein · 2014
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Context-dependent fine-grained entity type tagging
Dan Gillick, Nevena Lazic, Kuzman Ganchev, Jesse Kirchner, and David Huynh · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014
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Exploring semantic inter-class relationships (sir) for zero-shot action recognition
Chuang Gan, Ming Lin, Yi Yang, Yueting Zhuang, and Alexander G Hauptmann · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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An embarrassingly simple approach to zero-shot learning
Bernardino Romera-Paredes and Philip Torr · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Embedding methods for fine grained entity type classification
Dani Yogatama, Daniel Gillick, and Nevena Lazic · 2015
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Layer normalization
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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An empirical study and analysis of generalized zero-shot learning for object recognition in the wild
Wei-Lun Chao, Soravit Changpinyo, Boqing Gong, and Fei Sha · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Learning deep representations of fine-grained visual descriptions
Scott Reed, Zeynep Akata, Honglak Lee, and Bernt Schiele · 2016
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Afet: Automatic fine-grained entity typing by hierarchical partial-label embedding
Xiang Ren, Wenqi He, Meng Qu, Lifu Huang, Heng Ji, and Jiawei Han · 2016
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Latent embeddings for zero-shot classification
Yongqin Xian, Zeynep Akata, Gaurav Sharma, Quynh Nguyen, Matthias Hein, and Bernt Schiele · 2016
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Improving semantic parsing via answer type inference
Semih Yavuz, Izzeddin Gur, Yu Su, Mudhakar Srivatsa, and Xifeng Yan · 2016
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Graph convolutional encoders for syntax-aware neural machine translation
Joost Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, and Khalil Sima’an · 2017
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Google’s multilingual neural machine translation system: Enabling zero-shot translation
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
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Zero-shot learning across heterogeneous overlapping domains
Anjishnu Kumar, Pavankumar Reddy Muddireddy, Markus Dreyer, and Björn Hoffmeister · 2017
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Encoding sentences with graph convolutional networks for semantic role labeling
Diego Marcheggiani and Ivan Titov · 2017
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Dynamic routing between capsules
Sara Sabour, Nicholas Frosst, and Geoffrey E Hinton · 2017
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Neural architectures for fine-grained entity type classification
Sonse Shimaoka, Pontus Stenetorp, Kentaro Inui, and Sebastian Riedel · 2017
Cited alongside, same era.
Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Preserving semantic relations for zero-shot learning
Yashas Annadani and Soma Biswas · 2018
Cited alongside, same era.
Snips voice platform: an embedded spoken language understanding system for private-by-design voice interfaces
Alice Coucke, Alaa Saade, Adrien Ball, Théodore Bluche, Alexandre Caulier, David Leroy, Clément Doumouro, Thibault Gisselbrecht, Francesco Caltagirone, Thibaut Lavril, et al · 2018
Cited alongside, same era.
Allennlp: A deep semantic natural language processing platform
Learning dynamic knowledge graphs to generalize on text-based games
Ashutosh Adhikari, Xingdi Yuan, Marc-Alexandre Côté, Mikuláš Zelinka, Marc-Antoine Rondeau, Romain Laroche, Pascal Poupart, Jian Tang, Adam Trischler, and Will Hamilton · 2020
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Neural module networks for reasoning over text
Nitish Gupta, Kevin Lin, Dan Roth, Sameer Singh, and Matt Gardner · 2020
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Heterogeneous graph transformer
Ziniu Hu, Yuxiao Dong, Kuansan Wang, and Yizhou Sun · 2020
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Cskg: The commonsense knowledge graph
Filip Ilievski, Pedro Szekely, and Bin Zhang · 2020
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Transformers are graph neural networks
Chaitanya Joshi · 2020
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Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Peters, Michael Schmitz, and Luke S. Zettlemoyer · 2018
Cited alongside, same era.
Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling · 2018
Cited alongside, same era.
Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, L. Zhang, T. Xiang, P. Torr, and Timothy M. Hospedales · 2018
Cited alongside, same era.
Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
Cited alongside, same era.
Zero-shot recognition via semantic embeddings and knowledge graphs
Xiaolong Wang, Yufei Ye, and Abhinav Gupta · 2018
Cited alongside, same era.
Zero-shot user intent detection via capsule neural networks
Congying Xia, Chenwei Zhang, Xiaohui Yan, Yi Chang, and Philip S Yu · 2018
Cited alongside, same era.
Graph convolutional neural networks for web-scale recommender systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec · 2018
Cited alongside, same era.
Generalized zero-shot learning via over-complete distribution
Rohit Keshari, R. Singh, and Mayank Vatsa · 2020
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Domain-aware visual bias eliminating for generalized zero-shot learning
Shaobo Min, Hantao Yao, Hongtao Xie, Chaoqun Wang, Zheng-Jun Zha, and Yongdong Zhang · 2020
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Latent embedding feedback and discriminative features for zero-shot classification
Sanath Narayan, A. Gupta, F. Khan, Cees G. M. Snoek, and Ling Shao · 2020
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Unsupervised commonsense question answering with self-talk
Vered Shwartz, Peter West, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi · 2020
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Composition-based multi-relational graph convolutional networks
Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, and Partha Talukdar · 2020
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A meta-learning framework for generalized zero-shot learning
Vinay Kumar Verma, Dhanajit Brahma, and Piyush Rai · 2020
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Leveraging seen and unseen semantic relationships for generative zero-shot learning
M. R. Vyas, Hemanth Venkateswara, and S. Panchanathan · 2020
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Attribute prototype network for zero-shot learning
Wenjia Xu, Yongqin Xian, Jiuniu Wang, B. Schiele, and Zeynep Akata · 2020
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Episode-based prototype generating network for zero-shot learning
Y. Yu, Z. Ji, J. Han, and Z. Zhang · 2020
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Graph transformer networks
Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J Kim · 2020
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Transomcs: From linguistic graphs to commonsense knowledge
Hongming Zhang, Daniel Khashabi, Yangqiu Song, and Dan Roth · 2020
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Transzero++: Cross attribute-guided transformer for zero-shot learning
Shiming Chen, Zi-Quan Hong, Guosen Xie, Jian Zhao, Hao Li, Xinge You, Shuicheng Yan, and Ling Shao · 2021
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Adaptive and generative zero-shot learning
Yu-Ying Chou, Hsuan-Tien Lin, and Tyng-Luh Liu · 2021
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A generalization of transformer networks to graphs
Vijay Prakash Dwivedi and Xavier Bresson · 2021
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Contrastive embedding for generalized zero-shot learning
Zongyan Han, Zhenyong Fu, Shuo Chen, and Jian Yang · 2021
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Rethinking graph transformers with spectral attention
Devin Kreuzer, Dominique Beaini, Will Hamilton, Vincent Létourneau, and Prudencio Tossou · 2021
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Graphit: Encoding graph structure in transformers
Grégoire Mialon, Dexiong Chen, Margot Selosse, and Julien Mairal · 2021
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Modeling fine-grained entity types with box embeddings
Yasumasa Onoe, Michael Boratko, Andrew McCallum, and Greg Durrett · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
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Class normalization for zero-shot learning
Ivan Skorokhodov and Mohamed Elhoseiny · 2021
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Dual progressive prototype network for generalized zero-shot learning
Chaoqun Wang, Shaobo Min, Xuejin Chen, Xiaoyan Sun, and Houqiang Li · 2021
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2021
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Qa-gnn: Reasoning with language models and knowledge graphs for question answering
Michihiro Yasunaga, Hongyu Ren, Antoine Bosselut, Percy Liang, and Jure Leskovec · 2021
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Do transformers really perform badly for graph representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu · 2021
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Graph neural networks with learnable structural and positional representations
Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2022
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An entropy-guided reinforced partial convolutional network for zero-shot learning
Yun Li, Zhe Liu, L. Yao, Xianzhi Wang, Julian McAuley, and Xiaojun Chang · 2022
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