Awesome Fine-Grained Image Analysis – Papers, Codes and Datasets

Awesome Fine-Grained Image Analysis – Papers, Codes and Datasets

Table of contents

1. Introduction

2. Tutorials

3. Survey papers

4. Benchmark datasets

5. Fine-grained image recognition

   1. Fine-grained recognition by localization-classification subnetworks

      1. Employing detection or segmentation techniques

      2. Utilizing deep filters / activations

      3. Leveraging attention mechanisms

      4. Other methods

   2. Fine-grained recognition by end-to-end feature encoding

      1. High-order feature interactions

      2. Specific loss functions

      3. Other methods

   3. Fine-grained recognition with external information

      1. Fine-grained recognition with web data / auxiliary data

      2. Fine-grained recognition with multi-modality data

      3. Fine-grained recognition with humans in the loop

6. Fine-grained image retrieval

   1. Content-based fine-grained image retrieval

   2. Sketch-based fine-grained image retrieval

7. Future directions of FGIA

   1. Fine-grained few shot learning

   2. Fine-grained hashing

   3. Fine-grained domain adaptation

   4. Fine-grained image generation

   5. FGIA within more realistic settings

8. Recognition leaderboard

Introduction

This homepage lists some representative papers/codes/datasets all about deep learning based fine-grained image analysis, including fine-grained image recognition, fine-grained image retrieval, etc. If you have any questions, please feel free to contact Prof. Xiu-Shen Wei.

Tutorials

• Fine-Grained Image Analysis.
  Xiu-Shen Wei, and Jianxin Wu. Pacific Rim International Conference on Artificial Intelligence (PRICAI), 2018.

• Fine-Grained Image Analysis.
  Xiu-Shen Wei. IEEE International Conference on Multimedia and Expo (ICME), 2019.

Survey papers

• Deep Learning for Fine-Grained Image Analysis: A Survey.
  Xiu-Shen Wei, Jianxin Wu, and Quan Cui. arXiv: 1907.03069, 2019.

• A Survey on Deep Learning-based Fine-Grained Object Classification and Semantic Segmentation.
  Bo Zhao, Jiashi Feng, Xiao Wu, and Shuicheng Yan. International Journal of Automation and Computing, 2017.

Benchmark datasets

Summary of popular fine-grained image datasets. Note that ‘‘BBox’’ indicates whether this dataset provides object bounding box supervisions. ‘‘Part anno.’’ means providing the key part localizations. ‘‘HRCHY’’ corresponds to hierarchical labels. ‘‘ATR’’ represents the attribute labels (e.g., wing color, male, female, etc). ‘‘Texts’’ indicates whether fine-grained text descriptions of images are supplied.

Dataset name	Year	Meta-class	images	categories	BBox	Part anno.	HRCHY	ATR	Texts
Oxford flower	2008	Flowers	8,189	102
CUB200	2011	Birds	11,788	200
Stanford Dog	2011	Dogs	20,580	120
Stanford Car	2013	Cars	16,185	196
FGVC Aircraft	2013	Aircrafts	10,000	100
Birdsnap	2014	Birds	49,829	500
NABirds	2015	Birds	48,562	555
DeepFashion	2016	Clothes	800,000	1,050
Fru92	2017	Fruits	69,614	92
Veg200	2017	Vegetable	91,117	200
iNat2017	2017	Plants & Animals	859,000	5,089
RPC	2019	Retail products	83,739	200

Fine-grained image recognition

Fine-grained recognition by localization-classification subnetworks

Employing detection or segmentation techniques

• Part-based R-CNNs for Fine-Grained Category Detection.
  Ning Zhang, Jeff Donahue, Ross Girshick, and Trevor Darrell. ECCV, 2014. [code]

• Fine-Grained Recognition without Part Annotations.
  Jonathan Krause, Hailin Jin, Jianchao Yang, and Li Fei-Fei. CVPR, 2015. [code]

• Deep LAC: Deep Localization, Alignment and Classification for Fine-grained Recognition.
  Di Lin, Xiaoyong Shen, Cewu Lu, and Jiaya Jia. CVPR, 2015.

• Part-Stacked CNN for Fine-Grained Visual Categorization.
  Shaoli Huang, Zhe Xu, Dacheng Tao, and Ya Zhang. CVPR, 2016.

• 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. CVPR, 2016.

• Weakly Supervised Fine-Grained Categorization with Part-Based Image Representation.
  Yu Zhang, Xiu-Shen Wei, Jianxin Wu, Jianfei Cai, Jiangbo Lu, Viet-Anh Nguyen, and Minh N. Do. IEEE TIP, 2016.

• Coarse-to-Fine Description for Fine-Grained Visual Categorization.
  Hantao Yao, Shiliang Zhang, Yongdong Zhang, Jintao Li, and Qi Tian. IEEE TIP, 2016.

• Fine-Grained Recognition as HSnet Search for Informative Image Parts.
  Michael Lam, Behrooz Mahasseni, and Sinisa Todorovic. CVPR, 2017.

• Weakly Supervised Learning of Part Selection Model with Spatial Constraints for Fine-Grained Image Classification.
  Xiangteng He, and Yuxin Peng. AAAI, 2017.

• Mask-CNN: Localizing Parts and Selecting Descriptors for Fine-Grained Bird Species Categorization.
  Xiu-Shen Wei, Chen-Wei Xie, Jianxin Wu, and Zhi-Hua Zhou. Pattern Recognition, 2018.

• Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification From the Bottom Up.
  Weifeng Ge, Xiangru Lin, and Yizhou Yu. CVPR, 2019.

• Graph-Propagation Based Correlation Learning for Weakly Supervised Fine-Grained Image Classification.
  Zhihui Wang, Shijie Wang, Haojie Li, Zhi Dou, and Jianjun Li. AAAI, 2020.

• 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. AAAI, 2020.

Utilizing deep filters / activations

• 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. CVPR, 2015.

• The Treasure beneath Convolutional Layers: Cross-convolutional-layer Pooling for Image Classification.
  Lingqiao Liu, Chunhua Shen, and Anton van den Hengel. CVPR, 2015.

• Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks.
  Marcel Simon, and Erik Rodner. ICCV, 2015. [code]

• Picking Deep Filter Responses for Fine-grained Image Recognition.
  Xiaopeng Zhang, Hongkai, Xiong, Wengang Zhou, Weiyao Lin, and Qi Tian. CVPR, 2016.

• Learning a Discriminative Filter Bank within a CNN for Fine-grained Recognition.
  Yaming Wang, Vlad I. Morariu, and Larry S. Davis. CVPR, 2018. [code]

• Selective Sparse Sampling for Fine-grained Image Recognition.
  Yao Ding, Yanzhao Zhou, Yi Zhu, Qixiang Ye, and Jianbin Jiao. ICCV, 2019. [code]

• Interpretable and Accurate Fine-grained Recognition via Region Grouping.
  Zixuan Huang, and Yin Li. CVPR, 2020.

Leveraging attention mechanisms

• Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition.
  Jianlong Fu, Heliang Zheng, and Tao Mei. CVPR, 2017. [code]

• Learning Multi-Attention Convolutional Neural Network for Fine-Grained Image Recognition.
  Heliang Zheng, Jianlong Fu, Tao Mei, and Jiebo Luo. ICCV, 2017. [code]

• Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition.
  Xiao Liu, Jiang Wang, Shilei Wen, Errui Ding, and Yuanqing Lin. AAAI, 2017.

• Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition.
  Ming Sun, Yuchen Yuan, Feng Zhou, and Errui Ding. ECCV, 2018. [code]

• Object-Part Attention Model for Fine-Grained Image Classification.
  Yuxin Peng, Xiangteng He, and Junjie Zhao. IEEE TIP, 2018.

• Learning a Mixture of Granularity-Specific Experts for Fine-Grained Categorization.
  Lianbo Zhang, Shaoli Huang, Wei Liu, and Dacheng Tao. ICCV, 2019.

• Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-Grained Image Recognition.
  Heliang Zheng, Jianlong Fu, Zheng-Jun Zha, and Jiebo Luo. CVPR, 2019. [code]

• 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. CVPR, 2020. [code]

• Learning Rich Part Hierarchies With Progressive Attention Networks for Fine-Grained Image Recognition.
  Heliang Zheng, Jianlong Fu, Zheng-Jun Zha, Jiebo Luo, and Tao Mei. IEEE TIP, 2020.

Other methods

• Spatial Transformer Networks.
  Max Jaderberg, Karen Simonyan, Andrew Zisserman, and Koray Kavukcuoglu. NeurIPS, 2015. [code]

• Mining Discriminative Triplets of Patches for Fine-Grained Classification.
  Yaming Wang, Jonghyun Choi, Vlad I. Morariu, and Larry S. Davis. CVPR, 2016.

• Learning to Navigate for Fine-grained Classification.
  Ze Yang, Tiange Luo, Dong Wang, Zhiqiang Hu, Jun Gao, and Liwei Wang. ECCV, 2018. [code]

• Which and How Many Regions to Gaze: Focus Discriminative Regions for Fine-Grained Visual Categorization.
  Xiangteng He, Yuxin Peng, and Junjie Zhao. IJCV, 2019.

• Weakly Supervised Fine-grained Image Classification via Guassian Mixture Model Oriented Discriminative Learning.
  Zhihui Wang, Shijie Wang, Shuhui Yang, Haojie Li, Jianjun Li, and Zezhou Li. CVPR, 2020.

Fine-grained recognition by end-to-end feature encoding

High-order feature interactions

• Bilinear CNN Models for Fine-grained Visual Recognition.
  Tsung-Yu Lin, Aruni RoyChowdhury, and Subhransu Maji. ICCV, 2015. [code]

• Compact Bilinear Pooling.
  Yang Gao, Oscar Beijbom, Ning Zhang, and Trevor Darrell. CVPR, 2016. [code]

• Kernel Pooling for Convolutional Neural Networks.
  Yin Cui, Feng Zhou, Jiang Wang, Xiao Liu, Yuanqing Lin, and Serge Belongie. CVPR, 2017.

• Low-rank Bilinear Pooling for Fine-Grained Classification.
  Shu Kong, and Charless Fowlkes. CVPR, 2017. [code]

• Higher-order Integration of Hierarchical Convolutional Activations for Fine-Grained Visual Categorization.
  Sijia Cai, Wangmeng Zuo, and Lei Zhang. ICCV, 2017. [code]

• Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization.
  Peihua Li, Jiangtao Xie, Qilong Wang, and Zilin Gao. CVPR, 2018. [code]

• DeepKSPD: Learning Kernel-matrix-based SPD Representation for Fine-Grained Image Recognition.
  Melih Engin, Lei Wang, Luping Zhou, and Xinwang Liu. ECCV, 2018.

• Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition.
  Chaojian Yu, Xinyi Zhao, Qi Zheng, Peng Zhang, and Xinge You. ECCV, 2018. [code]

• Grassmann Pooling as Compact Homogeneous Bilinear Pooling for Fine-Grained Visual Classification.
  Xing Wei, Yue Zhang, Yihong Gong, Jiawei Zhang, and Nanning Zheng. ECCV, 2018.

• Learning Deep Bilinear Transformation for Fine-grained Image Representation.
  Heliang Zheng, Jianlong Fu, Zheng-Jun Zha, and Jiebo Luo. NeurIPS, 2019.

• Multi-Objective Matrix Normalization for Fine-Grained Visual Recognition.
  Shaobo Min, Hantao Yao, Hongtao Xie, Zheng-Jun Zha, and Yongdong Zhang. IEEE TIP, 2020.

Specific loss functions

• Maximum-Entropy Fine Grained Classification.
  Abhimanyu Dubey, Otkrist Gupta, Ramesh Raskar, and Nikhil Naik. NeurIPS, 2018.

• Pairwise Confusion for Fine-Grained Visual Classification.
  Abhimanyu Dubey, Otkrist Gupta, Pei Guo, Ramesh Raskar, Ryan Farrell, and Nikhil Naik. ECCV, 2018. [code]

• Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition.
  Ming Sun, Yuchen Yuan, Feng Zhou, and Errui Ding. ECCV, 2018. [code]

• Channel Interaction Networks for Fine-Grained Image Categorization.
  Yu Gao, Xintong Han, Xun Wang, Weilin Huang, and Matthew R. Scott. AAAI, 2020.

• Fine-grained Recognition: Accounting for Subtle Differences between Similar Classes.
  Guolei Sun, Hisham Cholakkal, Salman Khan, Fahad Shahbaz Khan, and Ling Shao. AAAI, 2020.

• Learning Attentive Pairwise Interaction for Fine-Grained Classification.
  Peiqin Zhuang, Yali Wang, and Yu Qiao. AAAI, 2020.

• The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification.
  Dongliang Chang, Yifeng Ding, Jiyang Xie, Ayan Kumar Bhunia, Xiaoxu Li, Zhanyu Ma, Ming Wu, Jun Guo, and Yi-Zhe Song. IEEE TIP, 2020.

Other methods

• Fine-Grained Image Classification by Exploring Bipartite-Graph Labels.
  Feng Zhou, and Yuanqing Lin. CVPR, 2016. [project page]

• Destruction and Construction Learning for Fine-grained Image Recognition.
  Yue Chen, Yalong Bai, Wei Zhang, and Tao Mei. CVPR, 2019. [code]

• Cross-X Learning for Fine-Grained Visual Categorization.
  Wei Luo, Xiong Yang, Xianjie Mo, Yuheng Lu, Larry S. Davis, Jun Li, Jian Yang, and Ser-Nam Lim. ICCV, 2019. [code]

• Fine-grained Image-to-Image Transformation towards Visual Recognition.
  Wei Xiong, Yutong He, Yixuan Zhang, Wenhan Luo, Lin Ma, and Jiebo Luo. CVPR, 2020.

• Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches.
  Ruoyi Du, Dongliang Chang, Ayan Kumar Bhunia, Jiyang Xie, Yi-Zhe Song, Zhanyu Ma, and Jun Guo. ECCV, 2020. [code]

Fine-grained recognition with external information

Fine-grained recognition with web data

• Hyper-Class Augmented and Regularized Deep Learning for Fine-Grained Image Classification.
  Saining Xie, Tianbao Yang, Xiaoyu Wang, and Yuanqing Lin. CVPR, 2015.

• Augmenting Strong Supervision Using Web Data for Fine-Grained Categorization.
  Zhe Xu, Shaoli Huang, Ya Zhang, and Dacheng Tao. ICCV, 2015.

• The unreasonable effectiveness of noisy data for fine-grained recognition.
  Jonathan Krause, Benjamin Sapp, Andrew Howard, Howard Zhou, Alexander Toshev, Tom Duerig, James Philbin, and Li Fei-Fei. ECCV, 2016.

• Webly Supervised Learning Meets Zero-shot Learning: A Hybrid Approach for Fine-grained Classification.
  Li Niu, Ashok Veeraraghavan, and Vshu Sabbarwal. CVPR, 2018.

• Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary Data.
  Yabin Zhang, Hui Tang, and Kai Jia. ECCV, 2018. [code]

• Recognition From Web Data: A Progressive Filtering Approach.
  Jufeng Yang, Xiaoxiao Sun, Yu-Kun Lai, Liang Zheng, and Ming-Ming Cheng. IEEE TIP, 2018.

• Webly-Supervised Fine-Grained Visual Categorization via Deep Domain Adaptation.
  Zhe Xu, Shaoli Huang, Ya Zhang, and Dacheng Tao. IEEE TPAMI, 2018.

• Learning from Web Data using Adversarial Discriminative Neural Networks for Fine-Grained Classification.
  Xiaoxiao Sun, Liyi Chen, and Jufeng Yang. AAAI, 2019.

• Web-Supervised Network with Softly Update-Drop Training for Fine-Grained Visual Classification.
  Chuanyi Zhang, Yazhou Yao, Huafeng Liu, Guo-Sen Xie, Xiangbo Shu, Tianfei Zhou, Zheng Zhang, Fumin Shen, and Zhenmin Tang. AAAI, 2020.

Fine-grained recognition with multi-modality data

• Fine-Grained Image Classification via Combining Vision and Language.
  Xiangteng He, and Yuxin Peng. CVPR, 2017.

• Audio Visual Attribute Discovery for Fine-Grained Object Recognition.
  Hua Zhang, Xiaochun Cao, and Rui Wang. AAAI, 2018.

• Fine-Grained Image Classification by Visual-Semantic Embedding.
  Huapeng Xu, Guilin Qi, Jingjing Li, Meng Wang, Kang Xu, and Huan Gao. IJCAI, 2018.

• Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition.
  Tianshui Chen, Liang Lin, Riquan Chen, Yang Wu, and Xiannan Luo. IJCAI, 2018.

• Bi-Modal Progressive Mask Attention for Fine-Grained Recognition.
  Kaitao Song, Xiu-Shen Wei, Xiangbo Shu, Ren-Jie Song, and Jianfeng Lu. IEEE TIP, 2020.

Fine-grained recognition with humans in the loop

• Fine-grained Categorization and Dataset Bootstrapping using Deep Metric Learning with Humans in the Loop.
  Yin Cui, Feng Zhou, Yuanqing Lin, and Serge Belongie. CVPR, 2016.

• Leveraging the Wisdom of the Crowd for Fine-Grained Recognition.
  Jia Deng, Jonathan Krause, Michael Stark, and Li Fei-Fei. IEEE TPAMI, 2016.

Fine-grained image retrieval

Content-based fine-grained image retrieval

• Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval.
  Xiu-Shen Wei, Jian-Hao Luo, Jianxin Wu, and Zhi-Hua Zhou. IEEE TIP, 2017. [project page]

• Centralized Ranking Loss with Weakly Supervised Localization for Fine-Grained Object Retrieval.
  Xiawu Zheng, Rongrong Ji, Xiaoshuai Sun, Yongjian Wu, Feiyue Huang, and Yanhua Yang. IJCAI, 2018.

• Towards Optimal Fine Grained Retrieval via Decorrelated Centralized Loss with Normalize-Scale layer.
  Xiawu Zheng, Rongrong Ji, Xiaoshuai Sun, Baochang Zhang, Yongjian Wu, and Feiyue Huang. AAAI, 2019.

• Fine-Grained Image Retrieval via Piecewise Cross Entropy loss.
  Xianxian Zeng, Yun Zhang, Xiaodong Wang, Kairui Chen, Dong Li, and Weijun Yang. Image and Vision Computing, 2020.

Sketch-based fine-grained image retrieval

• Fine-Grained Sketch-Based Image Retrieval by Matching Deformable Part Models.
  Yi Li, Timothy M. Hospedales, Yi-Zhe Song, and Shaogang Gong. BMVC, 2014.

• Sketch Me That Shoe.
  Qian Yu, Feng Liu, Yi-Zhe Song, Tao Xiang, Timothy M. Hospedales, and Chen Change Loy. CVPR, 2016.

• Cross-domain Generative Learning for Fine-Grained Sketch-Based Image Retrieval.
  Kaiyue Pang, Yi-Zhe Song, Tao Xiang, and Timothy M. Hospedales. BMVC, 2017.

• Deep Spatial-Semantic Attention for Fine-Grained Sketch-Based Image Retrieval.
  Jifei Song, Qian Yu, Yi-Zhe Song, Tao Xiang, and Timothy M. Hospedales. ICCV, 2017.

• Synergistic Instance-Level Subspace Alignment for Fine-Grained Sketch-Based Image Retrieval.
  Ke Li, Kaiyue Pang, Yi-Zhe Song, Timothy M. Hospedales, Tao Xiang, and Honggang Zhang. IEEE TIP, 2017.

• Generalising Fine-Grained Sketch-Based Image Retrieval.
  Kaiyue Pang, Ke Li, Yongxin Yang, Honggang Zhang, Timothy M. Hospedales, Tao Xiang, and Yi-Zhe Song. CVPR, 2019.

• Solving Mixed-modal Jigsaw Puzzle for Fine-Grained Sketch-Based Image Retrieval.
  Kaiyue Pang, Yongxin Yang, Timothy M. Hospedales, Tao Xiang, and Yi-Zhe Song. CVPR, 2020.

Future directions of FGIA

Fine-grained few shot learning

• Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examples.
  Xiu-Shen Wei, Peng Wang, Lingqiao Liu, Chunhua Shen, and Jianxin Wu. IEEE TIP, 2019.

• Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition.
  Satoshi Tsutsui, Yanwei Fu, and David Crandall. NeurIPS, 2019.

• Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition.
  Luming Tang, Davis Wertheimer, and Bharath Hariharan. CVPR, 2020. [code]

• Multi-attention Meta Learning for Few-shot Fine-grained Image Recognition.
  Yaohui Zhu, Chenlong Liu, and Shuqiang Jiang. IJCAI, 2020.

Fine-grained hashing

• ExchNet: A Unified Hashing Network for Large-Scale Fine-Grained Image Retrieval.
  Quan Cui, Qing-Yuan Jiang, Xiu-Shen Wei, Wu-Jun Li, and Osamu Yoshie. ECCV, 2020.

• Deep Saliency Hashing for Fine-Grained Retrieval.
  Sheng Jin, Hongxun Yao, Xiaoshuai Sun, Shangchen Zhu, Lei Zhang, and Xiansheng Hua. IEEE TIP, 2020.

Fine-grained domain adaptation

• Fine-grained Recognition in the Wild: A Multi-Task Domain Adaptation Approach.
  Timnit Geru, Judy Hoffman, and Li Fei-Fei. ICCV, 2017.

• Progressive Adversarial Networks for Fine-Grained Domain Adaptation.
  Sinan Wang, Xinyang Chen, Yunbo Wang, Mingsheng Long, and Jianmin Wang. CVPR, 2020.

• An Adversarial Domain Adaptation Network for Cross-Domain Fine-Grained Recognition.
  Yimu Wang, Ren-Jie Song, Xiu-Shen Wei, and Lijun Zhang. WACV, 2020.

Fine-grained image generation

• CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training.
  Jianmin Bao, Dong Chen, Fang Wen, Houqiang Li, and Gang Hua. ICCV, 2017. [code]

• AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks.
  Tao Xu, Pengchuan Zhang, Qiuyuan Huang, Han Zhang, Zhe Gan, Xiaolei Huang, and Xiaodong He. CVPR, 2018. [code]

• FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery.
  Krishna Kumar Singh, Utkarsh Ojha, and Yong Jae Lee. CVPR, 2019. [code]

FGIA within more realistic settings

• The iNaturalist Species Classification and Detection Dataset.
  Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie. CVPR, 2018.

• RPC: A Large-Scale Retail Product Checkout Dataset.
  Xiu-Shen Wei, Quan Cui, Lei Yang, Peng Wang, and Lingqiao Liu. arXiv: 1901.07249, 2019. [project page]

• Presence-Only Geographical Priors for Fine-Grained Image Classification.
  Oisin Mac Aodha, Elijah Cole, and Pietro Perona. ICCV, 2019.

Recognition leaderboard

The section is being continually updated. Since CUB200-2011 is the most popularly used fine-grained dataset, we list the fine-grained recognition leaderboard by treating it as the test bed.

Method	Published	BBox?	Part?	External information?	Base model	Image resolution	Accuracy
PB R-CNN	ECCV 2014				Alex-Net	224x224	73.9%
MaxEnt	NeurIPS 2018				GoogLeNet	TBD	74.4%
PB R-CNN	ECCV 2014				Alex-Net	224x224	76.4%
PS-CNN	CVPR 2016				CaffeNet	454x454	76.6%
MaxEnt	NeurIPS 2018				VGG-16	TBD	77.0%
Mask-CNN	PR 2018				Alex-Net	448x448	78.6%
PC	ECCV 2018				ResNet-50	TBD	80.2%
DeepLAC	CVPR 2015				Alex-Net	227x227	80.3%
MaxEnt	NeurIPS 2018				ResNet-50	TBD	80.4%
Triplet-A	CVPR 2016			Manual labour	GoogLeNet	TBD	80.7%
Multi-grained	ICCV 2015			WordNet etc.	VGG-19	224x224	81.7%
Krause et al.	CVPR 2015				CaffeNet	TBD	82.0%
Multi-grained	ICCV 2015			WordNet etc.	VGG-19	224x224	83.0%
TS	CVPR 2016				VGGD+VGGM	448x448	84.0%
Bilinear CNN	ICCV 2015				VGGD+VGGM	448x448	84.1%
STN	NeurIPS 2015				GoogLeNet+BN	448x448	84.1%
LRBP	CVPR 2017				VGG-16	224x224	84.2%
PDFS	CVPR 2016				VGG-16	TBD	84.5%
Xu et al.	ICCV 2015			Web data	CaffeNet	224x224	84.6%
Cai et al.	ICCV 2017				VGG-16	448x448	85.3%
RA-CNN	CVPR 2017				VGG-19	448x448	85.3%
MaxEnt	NeurIPS 2018				Bilinear CNN	TBD	85.3%
PC	ECCV 2018				Bilinear CNN	TBD	85.6%
CVL	CVPR 2017			Texts	VGG	TBD	85.6%
Mask-CNN	PR 2018				VGG-16	448x448	85.7%
GP-256	ECCV 2018				VGG-16	448x448	85.8%
KP	CVPR 2017				VGG-16	224x224	86.2%
T-CNN	IJCAI 2018				ResNet	224x224	86.2%
MA-CNN	ICCV 2017				VGG-19	448x448	86.5%
MaxEnt	NeurIPS 2018				DenseNet-161	TBD	86.5%
DeepKSPD	ECCV 2018				VGG-19	448x448	86.5%
OSME+MAMC	ECCV 2018				ResNet-101	448x448	86.5%
StackDRL	IJCAI 2018				VGG-19	224x224	86.6%
DFL-CNN	CVPR 2018				VGG-16	448x448	86.7%
Bi-Modal PMA	IEEE TIP 2020				VGG-16	448x448	86.8%
PC	ECCV 2018				DenseNet-161	TBD	86.9%
KERL	IJCAI 2018			Attributes	VGG-16	224x224	87.0%
HBP	ECCV 2018				VGG-16	448x448	87.1%
Mask-CNN	PR 2018				ResNet-50	448x448	87.3%
DFL-CNN	CVPR 2018				ResNet-50	448x448	87.4%
NTS-Net	ECCV 2018				ResNet-50	448x448	87.5%
HSnet	CVPR 2017				GoogLeNet+BN	TBD	87.5%
Bi-Modal PMA	IEEE TIP 2020				ResNet-50	448x448	87.5%
CIN	AAAI 2020				ResNet-50	448x448	87.5%
MetaFGNet	ECCV 2018			Auxiliary data	ResNet-34	TBD	87.6%
Cross-X	CVPR 2020				ResNet-50	448x448	87.7%
DCL	CVPR 2019				ResNet-50	448x448	87.8%
ACNet	CVPR 2020				VGG-16	448x448	87.8%
TASN	CVPR 2019				ResNet-50	448x448	87.9%
ACNet	CVPR 2020				ResNet-50	448x448	88.1%
CIN	AAAI 2020				ResNet-101	448x448	88.1%
DBTNet-101	NeurIPS 2019				ResNet-101	448x448	88.1%
Bi-Modal PMA	IEEE TIP 2020			Texts	VGG-16	448x448	88.2%
GCL	AAAI 2020				ResNet-50	448x448	88.3%
S3N	CVPR 2020				ResNet-50	448x448	88.5%
Sun et al.	AAAI 2020				ResNet-50	448x448	88.6%
FDL	AAAI 2020				ResNet-50	448x448	88.6%
Bi-Modal PMA	IEEE TIP 2020			Texts	ResNet-50	448x448	88.7%
DF-GMM	CVPR 2020				ResNet-50	448x448	88.8%
PMG	ECCV 2020				VGG-16	550x550	88.8%
FDL	AAAI 2020				DenseNet-161	448x448	89.1%
PMG	ECCV 2020				ResNet-50	550x550	89.6%
API-Net	AAAI 2020				DenseNet-161	512x512	90.0%
Ge et al.	CVPR 2019				GoogLeNet+BN	Shorter side is 800 px	90.3%

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