Awesome Fine-grained Visual Classification
- Fine-Grained Image Analysis with DeepLearning: A Survey (2021)
- Awesome Fine-Grained Image Analysis – Papers, Codes and Datasets
- Deep Learning for Fine-Grained Image Analysis: A Survey
- [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]
- [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]
- [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]
- 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]
- [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]
- [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]
- [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]
- [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]
- [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]
- [PB RCNN] Part-Based R-CNNs for Fine-Grained Category Detection (ECCV,2014) [paper]
- [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]
- [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]
- [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]
- [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]
- Multi-Level. (e.g., PMG / Cross-X / MGE-CNN)
- Multi-Scale. (e.g., RA-CNN / MGE-CNN / NTS-Net/ TransFG (overlap-split) )
| Method | Backbone | CUB(%) | CAR(%) | AIR(%) | DOG(%) |
|---|---|---|---|---|---|
| Krause |
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 |
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 |
- CUB-200-2011
- Stanford Dogs
- Stanford Cars
- FGVC Aircrafts
- RP2K
- Products-10k