Awesome Fine-grained Visual Classification

[Awesome Fine-grained Visual Classification](# Awesome Fine-grained Visual Classification)

• Survey
• Papers
• [Paper Summary](#Paper Summary)
• [Recognition leaderboard](#Recognition leaderboard)
• Workshops
• [Challenges or Competitions](#Challenges or Competitions)
• Datasets

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Survey

• Awesome Fine-Grained Image Analysis – Papers, Codes and Datasets
• Deep Learning for Fine-Grained Image Analysis: A Survey

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Papers

2021

• [FFVT] Feature Fusion Vision Transformer. (arxiv, 2021) [paper]
• [TPSKG] Transformer with Peak Suppression and Knowledge Guidance. (arxiv, 2021) [paper]
• [RAMS-Trans] RAMS-Trans: Recurrent Attention Multi-scale Transformer for Fine-grained Image Recognition. (arxiv, 2021) [paper]
• Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification. (ICCV, 2021) [paper] [code]
• [TransFG] TransFG: A Transformer Architecture for Fine-grained Recognition. (arxiv, 2021)[paper][code]
• Graph-Based High-Order Relation Discovery for Fine-Grained Recognition. (CVPR, 2021)[paper][code]
• Your "Flamingo" is My "Bird": Fine-Grained, or Not (CVPR, 2021)[paper]
• Discrimination-Aware Mechanism for Fine-Grained Representation Learning (CVPR, 2021)[paper]
• Neural Prototype Trees for Interpretable Fine-Grained Image Recognition (CVPR, 2021) [paper]
• Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification (AAAI, 2021) [paper]
• Intra-class Part Swapping for Fine-Grained Image Classification (WACV, 2021) [paper]

2020

• Interpretable and Accurate Fine-grained Recognition via Region Grouping (CVPR, 2020) [paper]
• [LIO] Look-into-Object: Self-supervised Structure Modeling for Object Recognition (CVPR, 2020) [paper][code]
• Weakly Supervised Fine-Grained Image Classification via Guassian Mixture Model Oriented Discriminative Learning [paper] [video]
• Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization (CVPR, 2020) [paper][code]
• [CIN] Channel Interaction Networks for Fine-Grained Image Categorization (AAAI, 2020) [paper]
• Graph-propagation based Correlation Learning for Weakly Supervised Fine-grained Image Classification (AAAI, 2020)
• [FDL] Filtration and Distillation: Enhancing Region Attention for Fine-Grained Visual Categorization (AAAI, 2020) [paper]
• [API-Net] Learning Attentive Pairwise Interaction for Fine-Grained Classification (AAAI, 2020) [paper]
• [PMG] Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches (ECCV, 2020)[paper]
• [MC-loss] The Devil is in the Channels Mutual-Channel Loss for Fine-Grained Image Classification (TIP, 2020) [paper] [code]

2019

• [TASN] Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognition (CVPR, 2019) [paper]
• Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom Up (CVPR, 2019)[paper]
• [Cross-X] Cross-X Learning for Fine-Grained Visual Categorization (ICCV, 2019) [paper]
• [DCL] Destruction and Construction Learning for Fine-grained Image Recognition (CVPR, 2019) [paper]
• [S3N] Selective Sparse Sampling for Fine-grained Image Recognition (ICCV, 2019) [paper](https://github.com/Yao-DD/S3N "code")]
• [MGE-CNN] Learning a Mixture of Granularity-Specific Experts for Fine-GrainedCategorization (ICCV, 2019)[paper]

2018

• [MAMC] Multi-Attention Multi-Class Constraint forFine-grained Image Recognition (ECCV, 2018)[paper]
• [PC] Pairwise Confusion for Fine-Grained Visual Classification (ECCV, 2018) [paper]
• [WSBAN] Weakly Supervised Bilinear Attention Network for Fine-Grained Visual Classification (unknown, 2018) [paper]
• [NTS-Net] Learning to Navigate for Fine-grained Classification (ECCV, 2018) [paper] [code]

2017

• [RA-CNN] Look Closer to See Better: Recurrent Attention Convolutional Neural Networkfor Fine-grained Image Recognition (CVPR, 2017) [paper]
• [MA-CNN] Learning Multi-Attention Convolutional Neural Network for Fine-GrainedImage Recognition (ICCV, 2017) [paper]

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Paper Summary

By localize and rescale techniques

• [RA-CNN] Look Closer to See Better: Recurrent Attention Convolutional Neural Networkfor Fine-grained Image Recognition (CVPR, 2017) [paper]
• [MA-CNN] Learning Multi-Attention Convolutional Neural Network for Fine-GrainedImage Recognition (ICCV, 2017) [paper]
• [NTS-Net] Learning to Navigate for Fine-grained Classification (ECCV, 2018) [paper] [code]
• [MGE-CNN] Learning a Mixture of Granularity-Specific Experts for Fine-GrainedCategorization (ICCV, 2019)[paper]
• [FDL] Filtration and Distillation: Enhancing Region Attention for Fine-Grained Visual Categorization (AAAI, 2020) [paper]

By metric learning

• [MAMC] Multi-Attention Multi-Class Constraint forFine-grained Image Recognition (ECCV, 2018)[paper]
• [PC] Pairwise Confusion for Fine-Grained Visual Classification (ECCV, 2018) [paper]
• [CIN] Channel Interaction Networks for Fine-Grained Image Categorization (AAAI, 2020) [paper]
• [API-Net] Learning Attentive Pairwise Interaction for Fine-Grained Classification (AAAI, 2020) [paper]

By Attention-based mertric learning

• [RA-CNN] Look Closer to See Better: Recurrent Attention Convolutional Neural Networkfor Fine-grained Image Recognition (CVPR, 2017) [paper]
• [MA-CNN] Learning Multi-Attention Convolutional Neural Network for Fine-GrainedImage Recognition (ICCV, 2017) [paper]
• [WSBAN] Weakly Supervised Bilinear Attention Network for Fine-Grained Visual Classification (unknown, 2018) [paper]
• [Cross-X] Cross-X Learning for Fine-Grained Visual Categorization (ICCV, 2019) [paper]
• [TASN] Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognition (CVPR, 2019) [paper]
• Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization (CVPR, 2020) [paper][code]

Transformer-based methods

• [TransFG] TransFG: A Transformer Architecture for Fine-grained Recognition. (arxiv, 2021)[paper][code]
• [FFVT] Feature Fusion Vision Transformer. (arxiv, 2021) [paper]
• [TPSKG] Transformer with Peak Suppression and Knowledge Guidance. (arxiv, 2021) [paper]
• [RAMS-Trans] RAMS-Trans: Recurrent Attention Multi-scale Transformer for Fine-grained Image Recognition. (arxiv, 2021) [paper]

General Useful Mechanism

• Multi-Level. (e.g., PMG / Cross-X / MGE-CNN)
• Multi-Scale. (e.g., RA-CNN / MGE-CNN / NTS-Net/ TransFG (overlap-split) )

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Recognition leaderboard

Method	Backbone	CUB(%)	CAR(%)	AIR(%)	DOG(%)
RA-CNN	VGG19	85.3	92.5	88.4	87.3
MA-CNN	VGG19	86.5	92.8	89.9	-
MAMC	ResNet101	86.5	93.0	-	85.2
PC	DenseNet161	86.9	92.9	89.2	83.8
FDL	DenseNet161	89.1	94.0	-	84.9
NTS-Net	ResNet50	87.5	93.9	91.4	-
Cross-X	ResNet50	87.7	94.6	-	88.9
S3N	ResNet50	88.5	94.7	92.8	-
DCL	ResNet50	87.8	94.5	93.0	-
TASN	ResNet50	87.9	93.8	-	-
PMG	ResNet50	89.6	95.1	93.4	-
CIN	ResNet50	88.1	94.5	92.8	-
API-Net	DenseNet161	90.0	95.3	93.9	89.4
LIO	ResNet50	88.0	94.5	92.7	-
TransFG	ViT-B/16	91.7	94.8	-	92.3

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Workshops

• The Eight Workshop on Fine-Grained Visual Categorization

Challenges or Competitions

• ACCV 2020 Webly-Supervised Fine-Grained Recognition Challenge

Datasets

• CUB-200-2011
• Stanford Dogs
• Stanford Cars
• FGVC Aircrafts
• RP2K
• Products-10k

Feel free to contact me if you find any interesting paper is missing.