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Awesome Fine-grained Visual Classification

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Awesome Fine-grained Visual Classification

Awesome Fine-grained Visual Classification

Survey

Papers

2023

  • [GIST] Generating Image-Specific Text for Fine-grained Object Classification (Arxiv, 2023) [paper]
  • Leveraging Vision-Language Foundation Models for Fine-Grained Downstream Tasks (Arxiv, 2023) [paper]
  • [M2Former] M2Former: Multi-Scale Patch Selection for Fine-Grained Visual Recognition (Arxiv, 2023) [paper]
  • [SM-ViT] Salient Mask-Guided Vision Transformer for Fine-Grained Classification (VISAPP, 2023) [paper]

2022

  • [DCAL] Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification (CVPR, 2022) [paper]
  • [SR-GNN] SR-GNN: Spatial Relation-Aware Graph Neural Network for Fine-Grained Image Categorization (CVPR, 2022) [paper]
  • [ViT-FOD] ViT-FOD: A Vision Transformer based Fine-grained Object Discriminator (arxiv, 2022) [paper]

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]
  • A free lunch from ViT:Adaptive Attention Multi-scale Fusion Transformer for Fine-grained Visual Recognition (arxiv, 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] [code]
  • [MGE-CNN] Learning a Mixture of Granularity-Specific Experts for Fine-GrainedCategorization (ICCV, 2019)[paper]
  • [DBTNet-50] Learning Deep Bilinear Transformation for Fine-grained Image Representation (NeuIPS, 2019)[paper]

2018

  • [MAMC] Multi-Attention Multi-Class Constraint for Fine-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]
  • [OPAM] Object-Part Attention Model for Fine-grained Image Classification (IEEE TIP, 2018) [paper]

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]
  • [Cai $et~al.$] Higher-order Integration of Hierarchical Convolutional Activations for Fine-grained Visual Categorization (ICCV, 2017) [paper]
  • [KP] Kernel Pooling for Convolutional Neural Networks (CVPR, 2017) [paper]

2016

  • [C-BCNN] Compact Bilinear Pooling (CVPR,2016) [paper] [code]
  • [SPDA-CNN] SPDA-CNN: Unifying Semantic Part Detection and Abstraction for Fine-grained Recognition (CVPR,2016) [paper]
  • [PS-CNN] Part-Stacked CNN for Fine-Grained Visual Categorization (CVPR,2016) [paper]

2015

  • [BCNN] Bilinear CNN Models for Fine-grained Visual Recognition (ICCV,2015) [paper] [code]
  • [Krause $et~al.$] Fine-Grained Recognition without Part Annotations (CVPR,2015) [paper]
  • [Deep LAC] Deep LAC: Deep Localization, Alignment and Classificationfor Fine-grained Recognition (CVPR,2015) [paper]
  • [CL] The Treasure beneath Convolutional Layers：Cross-convolutional-layer Pooling for Image Classification (CVPR,2015) [paper]

2014

  • [PB RCNN] Part-Based R-CNNs for Fine-Grained Category Detection (ECCV,2014) [paper]

Paper Summary

By localize and rescale techniques

  • [PB R-CNN] Part-Based R-CNNs for Fine-Grained Category Detection (ECCV,2014) [paper]
  • [Krause $et~al.$] Fine-Grained Recognition without Part Annotations (CVPR,2015) [paper]
  • [Deep LAC] Deep LAC: Deep Localization, Alignment and Classificationfor Fine-grained Recognition (CVPR,2015) [paper]
  • [PS-CNN] Part-Stacked CNN for Fine-Grained Visual Categorization (CVPR,2016) [paper]
  • [SPDA-CNN] SPDA-CNN: Unifying Semantic Part Detection and Abstraction for Fine-grained Recognition (CVPR,2016) [paper]
  • [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

  • [BCNN] Bilinear CNN Models for Fine-grained Visual Recognition (ICCV,2015) [paper] [code]
  • [CL] The Treasure beneath Convolutional Layers：Cross-convolutional-layer Pooling for Image Classification (CVPR,2015) [paper]
  • [C-BCNN] Compact Bilinear Pooling (CVPR,2016) [paper] [code]
  • [Cai $et~al.$] Higher-order Integration of Hierarchical Convolutional Activations for Fine-grained Visual Categorization (ICCV, 2017) [paper]
  • [KP] Kernel Pooling for Convolutional Neural Networks (CVPR, 2017) [paper]
  • [MAMC] Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition (ECCV, 2018)[paper]
  • [PC] Pairwise Confusion for Fine-Grained Visual Classification (ECCV, 2018) [paper]
  • [DBTNet-50] Learning Deep Bilinear Transformation for Fine-grained Image Representation (NeuIPS, 2019)[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 methods

  • [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]
  • [OPAM] Object-Part Attention Model for Fine-grained Image Classification (IEEE TIP, 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]
  • Interpretable and Accurate Fine-grained Recognition via Region Grouping (CVPR, 2020) [paper]
  • Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization (CVPR, 2020) [paper][code]
  • [SR-GNN] SR-GNN: Spatial Relation-Aware Graph Neural Network for Fine-Grained Image Categorization (CVPR, 2022) [paper]
  • Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification. (ICCV, 2021) [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]
  • [ViT-FOD] ViT-FOD: A Vision Transformer based Fine-grained Object Discriminator (arxiv, 2022) [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) )

Recognition leaderboard

Method Backbone CUB(%) CAR(%) AIR(%) DOG(%)
Krause $et~al.$ CaffeNet 75.7 - - -
PS-CNN CaffeNet 76.6 - - -
CL Alex-Net 73.5 - - -
PB R-CNN Alex-Net 76.4 - - -
Deep LAC Alex-Net 80.3 - - -
BCNN VGG16+VGG-M 84.1 91.3 84.1 -
C-BCNN VGG16 84.3 91.2 84.1 -
SPDA-CNN VGG16 85.1 - - -
Cai $et~al.$ VGG16 85.3 91.7 88.3 -
OPAM VGG16 85.8 92.2 - -
KP VGG16 86.2 92.4 86.9 -
DPTNet-50 VGG16 87.5 94.1 91.2 -
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
ViT_FOD ViT-B/16 91.9 - - 93.0

Workshops

Challenges or Competitions

Datasets

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

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Awesome Fine-grained Visual Classification