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Recognizing objects from subcategories with very subtle differences remains a challenging task due to the large intra-class and small inter-class variation.
Novel dataset for fine-grained image categorization
Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, and Fei-Fei Li · 2011
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Fei-Fei Li · 2013
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Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
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Bird species categorization using pose normalized deep convolutional nets
Steve Branson, Grant Van Horn, Serge Belongie, and Pietro Perona · 2014
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Understanding deep architectures using a recursive convolutional network
David Eigen, Jason Rolfe, Rob Fergus, and Yann LeCun · 2014
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Distilling the knowledge in a neural network
Hinton Geoffrey, Oriol Vinyals, and Jeff Dean · 2015
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Hypercolumns for object segmentation and fine-grained localization
Bharath Hariharan, Pablo Arbeláez, Ross Girshick, and Jitendra Malik · 2015
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Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection
Grant Van Horn, Steve Branson, Ryan Farrell, Scott Haber, Jessie Barry, Panos Ipeirotis, Pietro Perona, and Serge Belongie · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Deep lac: Deep localization, alignment and classification for fine-grained recognition
Di Lin, Xiaoyong Shen, Cewu Lu, and Jiaya Jia · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Neural activation constellations: Unsupervised part model discovery with convolutional networks
Marcel Simon and Erik Rodner · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Cited alongside, same era.
Multiple granularity descriptors for fine-grained categorization
Dequan Wang, Zhiqiang Shen, Jie Shao, Wei Zhang, Xiangyang Xue, and Zheng Zhang · 2015
Cited alongside, same era.
The application of two-level attention models in deep convolutional neural network for fine-grained image classification
Tianjun Xiao, Yichong Xu, Kuiyuan Yang, Jiaxing Zhang, Yuxin Peng, and Zheng Zhang · 2015
Cited alongside, same era.
Hyper-class augmented and regularized deep learning for fine-grained image classification
Saining Xie, Tianbao Yang, Xiaoyu Wang, and Yuanqing Lin · 2015
Cited alongside, same era.
Augmenting strong supervision using web data for fine-grained categorization
Zhe Xu, Shaoli Huang, Ya Zhang, and Dacheng Tao · 2015
Cited alongside, same era.
Kernel pooling for convolutional neural networks
Yin Cui, Feng Zhou, Jiang Wang, Xiao Liu, Yuanqing Lin, and Serge Belongie · 2017
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Recurrent attention convolutional neural network for fine-grained image recognition
Jianlong Fu, Heliang Zheng, and Tao Mei · 2017
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Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q. Weinberger · 2017
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Dynamic computational time for visual attention
Zhichao Li, Yi Yang, Xiao Liu, Feng Zhou, Shilei Wen, and Wei Xu · 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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A unified multi-scale deep convolutional neural network for fast object detection
Zhaowei Cai, Quanfu Fan, Rogerio S. Feris, and Nuno Vasconcelos · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Hypernet: Towards accurate region proposal generation and joint object detection
Tao Kong, Anbang Yao, Yurong Chen, and Fuchun Sun · 2016
Cited alongside, same era.
Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg · 2016
Cited alongside, same era.
Fully convolutional attention localization networks
Xiao Liu, Tian Xia, Jian Wang, and Yuanqing Lin · 2016
Cited alongside, same era.
Spda-cnn: Unifying semantic part detection and abstraction for fine-grained recognition
Han Zhang, Tao Xu, Mohamed Elhoseiny, Xiaolei Huang, Shaoting Zhang, Ahmed Elgammal, and Dimitris Metaxas · 2016
Cited alongside, same era.
Picking deep filter responses for fine-grained image recognition
Xiaopeng Zhang, Hongkai Xiong, Wengang Zhou, Weiyao Lin, and Qi Tian · 2016
Cited alongside, same era.
Localizing by describing: Attribute-guided attention localization for fine-grained recognition
Xiao Liu, Jiang Wang, Shilei Wen, Errui Ding, and Yuanqing Lin · 2017
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Sampling matters in deep embedding learning
Chao-Yuan Wu, R Manmatha, Alexander J Smola, and Philipp Krahenbuhl · 2017
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Learning multi-attention convolutional neural network for fine-grained image recognition
Heliang Zheng, Jianlong Fu, Tao Mei, and Jiebo Luo · 2017
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Maximum entropy fine-grained classification
Abhimanyu Dubey, Otkrist Gupta, Ramesh Raskar, and Nikhil Naik · 2018
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Multi-attention multi-class constraint for fine-grained image recognition
Ming Sun, Yuchen Yuan, Feng Zhou, and Errui Ding · 2018
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Learning a discriminative filter bank within a cnn for fine-grained recognition
Yaming Wang, Vlad I. Morariu, and Larry S. Davis · 2018
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Learning to navigate for fine-grained classification
Ze Yang, Tiange Luo, Dong Wang, Zhiqiang Hu, Jun Gao, and Liwei Wang · 2018
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Deep layer aggregation
Fisher Yu, Dequan Wang, Evan Shelhamer, and Trevor Darrell · 2018
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