Fine-Grained(细粒度) Image – Papers, Codes and Datasets

可紊 提交于 2019-12-03 17:18:55

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

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

    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. Unsupervised with pre-trained models

    2. Supervised with metric learning

  7. Fine-grained image generation

    1. Generating from fine-grained image distributions

    2. Generating from text descriptions

  8. Future directions of FGIA

    1. Automatic fine-grained models

    2. Fine-grained few shot learning

    3. Fine-grained hashing

    4. FGIA within more realistic settings

  9. Leaderboard

Introduction

This homepage lists some representative papers/codes/datasets all about deep learning based fine-grained image, including fine-grained image recognition, fine-grained image retrieval, fine-grained image generation, etc. If you have any questions, please feel free to leave message.

Survey papers

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

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

Fine-grained recognition with external information

Fine-grained recognition with web data / auxiliary data

Fine-grained recognition with multi-modality data

Fine-grained recognition with humans in the loop

Fine-grained image retrieval

Unsupervised with pre-trained models

Supervised with metric learning

Fine-grained image generation

Generating from fine-grained image distributions

Generating from text descriptions

Future directions of FGIA

Fine-grained few shot learning

FGIA within more realistic settings

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 Publication BBox? Part? External information? Base model Image resolution Accuracy
PB R-CNN ECCV 2014       Alex-Net 224x224 73.9%
MaxEnt NIPS 2018       GoogLeNet TBD 74.4%
PB R-CNN ECCV 2014

    Alex-Net 224x224 76.4%
PS-CNN CVPR 2016

  CaffeNet 454x454 76.6%
MaxEnt NIPS 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 NIPS 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 NIPS 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 NIPS 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 NIPS 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%
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%
MetaFGNet ECCV 2018     Auxiliary data ResNet-34 TBD 87.6%
DCL CVPR 2019       ResNet-50 448x448 87.8%
TASN CVPR 2019       ResNet-50 448x448 87.9%
Ge et al. CVPR 2019       GoogLeNet+BN Shorter side is 800 px 90.4%

标签
易学教程内所有资源均来自网络或用户发布的内容,如有违反法律规定的内容欢迎反馈
该文章没有解决你所遇到的问题?点击提问,说说你的问题,让更多的人一起探讨吧!