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The core for tackling the fine-grained visual categorization (FGVC) is to learn subtle yet discriminative features.
Face recognition using line edge map
Yongsheng Gao and Maylor KH Leung · 2002
Earlier work this paper cites.
Ranking measures and loss functions in learning to rank
Wei Chen, Tie-Yan Liu, Yanyan Lan, Zhi-Ming Ma, and Hang Li · 2009
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Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor
Baochang Zhang, Yongsheng Gao, Sanqiang Zhao, and Jianzhuang Liu · 2009
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Novel dataset for fine-grained image categorization: Stanford dogs
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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Poof: Part-based one-vs.-one features for fine-grained categorization, face verification, and attribute estimation
Thomas Berg and Peter N Belhumeur · 2013
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Hierarchical part matching for fine-grained visual categorization
Lingxi Xie, Qi Tian, Richang Hong, Shuicheng Yan, and Bo Zhang · 2013
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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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
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Leaf image retrieval using combined feature of vein and contour
Xiaohan Yu, Shengwu Xiong, and Yongsheng Gao · 2015
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Part-stacked cnn for fine-grained visual categorization
Shaoli Huang, Zhe Xu, Dacheng Tao, and Ya Zhang · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Multiscale crossing representation using combined feature of contour and venation for leaf image identification
Xiaohan Yu, Shengwu Xiong, Yongsheng Gao, Yang Zhao, and Xiaohui Yuan · 2016
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Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition
Jianlong Fu, Heliang Zheng, and Tao Mei · 2017
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Weakly supervised learning of part selection model with spatial constraints for fine-grained image classification
Xiangteng He and Yuxin Peng · 2017
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Improved bilinear pooling with cnns
Tsung-Yu Lin and Subhransu Maji · 2017
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Diversified visual attention networks for fine-grained object classification
Bo Zhao, Xiao Wu, Jiashi Feng, Qiang Peng, and Shuicheng Yan · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
Maximum-entropy fine-grained classification
Abhimanyu Dubey, Otkrist Gupta, Ramesh Raskar, and Nikhil Naik · 2018
Cited alongside, same era.
Towards faster training of global covariance pooling networks by iterative matrix square root normalization
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Fine-grained visual classification via progressive multi-granularity training of jigsaw patches
Ruoyi Du, Dongliang Chang, Ayan Kumar Bhunia, Jiyang Xie, Zhanyu Ma, Yi-Zhe Song, and Jun Guo · 2020
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Channel interaction networks for fine-grained image categorization
Yu Gao, Xintong Han, Xun Wang, Weilin Huang, and Matthew Scott · 2020
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Attention convolutional binary neural tree for fine-grained visual categorization
Ruyi Ji, Longyin Wen, Libo Zhang, Dawei Du, Yanjun Wu, Chen Zhao, Xianglong Liu, and Feiyue Huang · 2020
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Filtration and distillation: Enhancing region attention for fine-grained visual categorization
Chuanbin Liu, Hongtao Xie, Zheng-Jun Zha, Lingfeng Ma, Lingyun Yu, and Yongdong Zhang · 2020
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Peihua Li, Jiangtao Xie, Qilong Wang, and Zilin Gao · 2018
Cited alongside, same era.
Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Cited alongside, same era.
Learning a discriminative filter bank within a cnn for fine-grained recognition
Yaming Wang, Vlad I Morariu, and Larry S Davis · 2018
Cited alongside, same era.
Grassmann pooling as compact homogeneous bilinear pooling for fine-grained visual classification
Xing Wei, Yue Zhang, Yihong Gong, Jiawei Zhang, and Nanning Zheng · 2018
Cited alongside, same era.
Learning to navigate for fine-grained classification
Ze Yang, Tiange Luo, Dong Wang, Zhiqiang Hu, Jun Gao, and Liwei Wang · 2018
Cited alongside, same era.
Destruction and construction learning for fine-grained image recognition
Yue Chen, Yalong Bai, Wei Zhang, and Tao Mei · 2019
Cited alongside, same era.
Selective sparse sampling for fine-grained image recognition
Yao Ding, Yanzhao Zhou, Yi Zhu, Qixiang Ye, and Jianbin Jiao · 2019
Cited alongside, same era.
Weakly supervised complementary parts models for fine-grained image classification from the bottom up
Weifeng Ge, Xiangru Lin, and Yizhou Yu · 2019
Cited alongside, same era.
Later among the works it cites.
Learning semantically enhanced feature for fine-grained image classification
Wei Luo, Hengmin Zhang, Jun Li, and Xiu-Shen Wei · 2020
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Fine-grained recognition: Accounting for subtle differences between similar classes
Guolei Sun, Hisham Cholakkal, Salman Khan, Fahad Khan, and Ling Shao · 2020
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2020
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Patchy image structure classification using multi-orientation region transform
Xiaohan Yu, Yang Zhao, Yongsheng Gao, Shengwu Xiong, and Xiaohui Yuan · 2020
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Learning attentive pairwise interaction for fine-grained classification
Peiqin Zhuang, Yali Wang, and Yu Qiao · 2020
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Training vision transformers for image retrieval
Alaaeldin El-Nouby, Natalia Neverova, Ivan Laptev, and Hervé Jégou · 2021
Closest in time.
Transfg: A transformer architecture for fine-grained recognition
Ju He, Jie-Neng Chen, Shuai Liu, Adam Kortylewski, Cheng Yang, Yutong Bai, Changhu Wang, and Alan Yuille · 2021
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EAR-NET: Error Attention Refining Network For Retinal Vessel Segmentation
Jun Wang, Zhao Yang, Linglong Qian, Xiaohan Yu, and Yongsheng Gao · 2021
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Mask guided attention for fine-grained patchy image classification
Jun Wang, Xiaohan Yu, and Yongsheng Gao · 2021
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Learning deep part-aware embedding for person retrieval
Yang Zhao, Chunhua Shen, Xiaohan Yu, Hao Chen, Yongsheng Gao, and Shengwu Xiong · 2021
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Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers
Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip HS Torr, et al · 2021
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Learning discriminative region representation for person retrieval
Yang Zhao, Xiaohan Yu, Yongsheng Gao, and Chunhua Shen · 2022
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