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Object detection is considered as one of the most challenging problems in computer vision, since it requires correct prediction of both classes and locations of objects in images.
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
Alireza Farhadi, Ian Endres, Derek Hoiem, and David Forsyth · 2009
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Learning to detect unseen object classes by between-class attribute transfer
C. H. Lampert, H. Nickisch, and S. Harmeling · 2009
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The pascal visual object classes (VOC) challenge
Mark Everingham, Luc Gool, Christopher K. I. Williams, John Winn, and Andrew Zisserman · 2010
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Large scale image annotation: learning to rank with joint word-image embeddings
Jason Weston, Samy Bengio, and Nicolas Usunier · 2010
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Evaluating knowledge transfer and zero-shot learning in a large-scale setting
Marcus Rohrbach, Michael Stark, and Bernt Schiele · 2011
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Label-embedding for attribute-based classification
Zeynep Akata, Florent Perronnin, Zaid Harchaoui, and Cordelia Schmid · 2013
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Write a classifier: Zero-shot learning using purely textual descriptions
Mohamed Elhoseiny, Babak Saleh, and Ahmed Elgammal · 2013
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Devise: A deep visual-semantic embedding model
Andrea Frome, Greg S Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Tomas Mikolov, et al · 2013
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Zero-shot learning by convex combination of semantic embeddings
Mohammad Norouzi, Tomas Mikolov, Samy Bengio, Yoram Singer, Jonathon Shlens, Andrea Frome, Greg S Corrado, and Jeffrey Dean · 2013
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Large-scale object classification using label relation graphs
Jia Deng, Nan Ding, Yangqing Jia, Andrea Frome, Kevin Murphy, Samy Bengio, Yuan Li, Hartmut Neven, and Hartwig Adam · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
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Lsda: Large scale detection through adaptation
Judy Hoffman, Sergio Guadarrama, Eric S Tzeng, Ronghang Hu, Jeff Donahue, Ross Girshick, Trevor Darrell, and Kate Saenko · 2014
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Zero-shot recognition with unreliable attributes
Dinesh Jayaraman and Kristen Grauman · 2014
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Attribute-based classification for zero-shot visual object categorization
C.H. Lampert, H. Nickisch, and S. Harmeling · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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Overfeat: Integrated recognition, localization and detection using convolutional networks
Pierre Sermanet, David Eigen, Xiang Zhang, Michaël Mathieu, Robert Fergus, and Yann Lecun · 2014
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The fastest deformable part model for object detection
Junjie Yan, Zhen Lei, Longyin Wen, and Stan Li · 2014
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Evaluation of output embeddings for fine-grained image classification
Zeynep Akata, Scott Reed, Daniel Walter, Honglak Lee, and Bernt Schiele · 2015
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How to transfer? zero-shot object recognition via hierarchical transfer of semantic attributes
Ziad Al-Halah and Rainer Stiefelhagen · 2015
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Predicting deep zero-shot convolutional neural networks using textual descriptions
You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
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Attributes2classname: A discriminative model for attribute-based unsupervised zero-shot learning
Berkan Demirel, Ramazan Gokberk Cinbis, and Nazli Ikizler-Cinbis · 2017
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Low-rank embedded ensemble semantic dictionary for zero-shot learning
Zhengming Ding, Ming Shao, and Yun Fu · 2017
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Semantic autoencoder for zero-shot learning
Elyor Kodirov, Tao Xiang, and Shaogang Gong · 2017
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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
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Yolo9000: Better, faster, stronger
Joseph Redmon and Ali Farhadi · 2017
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Jimmy Ba, Kevin Swersky, Sanja Fidler, and Ruslan Salakhutdinov · 2015
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Fast r-cnn
Ross Girshick · 2015
Cited alongside, same era.
Spatial pyramid pooling in deep convolutional networks for visual recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Cited alongside, same era.
Detector discovery in the wild: Joint multiple instance and representation learning
Judy Hoffman, Deepak Pathak, Trevor Darrell, and Kate Saenko · 2015
Cited alongside, same era.
Densebox: Unifying landmark localization with end to end object detection
Lichao Huang, Yi Yang, Yafeng Deng, and Yinan Yu · 2015
Cited alongside, same era.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
Cited alongside, same era.
An embarrassingly simple approach to zero-shot learning
Bernardino Romera-Paredes and PHS Torr · 2015
Cited alongside, same era.
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Matrix tri-factorization with manifold regularizations for zero-shot learning
Xing Xu, Fumin Shen, Yang Yang, Dongxiang Zhang, Heng Tao Shen, and Jingkuan Song · 2017
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Ankan Bansal, Karan Sikka, Gaurav Sharma, Rama Chellappa, and Ajay Divakaran · 2018
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Zero-shot learning with attribute selection
Yuchen Guo, Guiguang Ding, Jungong Han, and Sheng Tang · 2018
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Zero-shot learning via attribute regression and class prototype rectification
Changzhi Luo, Zhetao Li, Kaizhu Huang, Jiashi Feng, and Meng Wang · 2018
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Zero-shot object detection: Learning to simultaneously recognize and localize novel concepts
Shafin Rahman, Salman Khan, and Fatih Porikli · 2018
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Transductive unbiased embedding for zero-shot learning
Jie Song, Chengchao Shen, Yezhou Yang, Yang Liu, and Mingli Song · 2018
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Pengkai Zhu, Hanxiao Wang, Tolga Bolukbasi, and Venkatesh Saligrama · 2018
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