SOTAVerified

Fine-Grained Image Classification

Fine-Grained Image Classification is a task in computer vision where the goal is to classify images into subcategories within a larger category. For example, classifying different species of birds or different types of flowers. This task is considered to be fine-grained because it requires the model to distinguish between subtle differences in visual appearance and patterns, making it more challenging than regular image classification tasks.

( Image credit: Looking for the Devil in the Details )

Papers

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Showing 101–150 of 353 papers

TitleStatusHype
MergedNET: A simple approach for one-shot learning in siamese networks based on similarity layersCode0
Learning Multi-Subset of Classes for Fine-Grained Food RecognitionCode0
Enhancing Fine-Grained 3D Object Recognition using Hybrid Multi-Modal Vision Transformer-CNN ModelsCode0
Fine-grained Classification of Solder Joints with α-skew Jensen-Shannon Divergence—0
A Continual Development Methodology for Large-scale Multitask Dynamic ML SystemsCode0
Bag of Tricks and a Strong Baseline for FGVCCode0
SR-GNN: Spatial Relation-aware Graph Neural Network for Fine-Grained Image CategorizationCode1
SIM-Trans: Structure Information Modeling Transformer for Fine-grained Visual CategorizationCode1
Conviformers: Convolutionally guided Vision TransformerCode0
Preserving Fine-Grain Feature Information in Classification via Entropic RegularizationCode0
Convolutional Fine-Grained Classification with Self-Supervised Target Relation RegularizationCode1
Visual correspondence-based explanations improve AI robustness and human-AI team accuracyCode1
Explored An Effective Methodology for Fine-Grained Snake RecognitionCode0
Few-shot Fine-grained Image Classification via Multi-Frequency Neighborhood and Double-cross ModulationCode0
ViT-NeT: Interpretable Vision Transformers with Neural Tree DecoderCode1
Contrastive Deep SupervisionCode1
Adaptive Fine-Grained Predicates Learning for Scene Graph Generation—0
Learning Cross-Image Object Semantic Relation in Transformer for Few-Shot Fine-Grained Image ClassificationCode1
0/1 Deep Neural Networks via Block Coordinate Descent—0
Multi-View Active Fine-Grained RecognitionCode0
On the Eigenvalues of Global Covariance Pooling for Fine-grained Visual RecognitionCode0
An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning SystemsCode0
Fine-Grained Visual Classification using Self Assessment ClassifierCode1
Large Neural Networks Learning from Scratch with Very Few Data and without Explicit Regularization—0
Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification—0
Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition—0
Fine-Grained Predicates Learning for Scene Graph GenerationCode1
Knowledge Mining with Scene Text for Fine-Grained RecognitionCode1
ViT-FOD: A Vision Transformer based Fine-grained Object Discriminator—0
Three things everyone should know about Vision TransformersCode0
Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine SynergyCode1
Automatic Fine-grained Glomerular Lesion Recognition in Kidney Pathology—0
Dynamic MLP for Fine-Grained Image Classification by Leveraging Geographical and Temporal InformationCode1
MetaFormer: A Unified Meta Framework for Fine-Grained RecognitionCode2
Ensembles of Vision Transformers as a New Paradigm for Automated Classification in EcologyCode0
Bridge the Gap between Supervised and Unsupervised Learning for Fine-Grained Classification—0
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without SupervisionCode0
Image Difference Captioning with Pre-training and Contrastive LearningCode1
A Novel Plug-in Module for Fine-Grained Visual ClassificationCode2
Dataset Condensation with Contrastive SignalsCode1
Revisiting Weakly Supervised Pre-Training of Visual Perception ModelsCode1
Label Relation Graphs Enhanced Hierarchical Residual Network for Hierarchical Multi-Granularity ClassificationCode1
A Simple Episodic Linear Probe Improves Visual Recognition in the WildCode2
Cross-Part Learning for Fine-Grained Image ClassificationCode0
Progressive Multi-stage Interactive Training in Mobile Network for Fine-grained Recognition—0
Making a Bird AI Expert Work for You and MeCode1
Clue Me In: Semi-Supervised FGVC with Out-of-Distribution DataCode1
ML-Decoder: Scalable and Versatile Classification HeadCode1
Improved Robustness of Vision Transformer via PreLayerNorm in Patch Embedding—0
High-Order-Interaction for weakly supervised Fine-Grained Visual CategorizationCode0
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All datasetsStanford CarsFGVC-AircraftCUB-200-2011NABirdsOxford 102 FlowersStanford DogsOxford-IIIT PetsCaltech-101Food-101Oxford-IIIT Pet DatasetCompCarsBird-225

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TResnet-L + PMDAccuracy97.3—Unverified
2CMAL-NetAccuracy97.1—Unverified
3I2-HOFIAccuracy96.92—Unverified
4TResNet-L + ML-DecoderAccuracy96.41—Unverified
5DATAccuracy96.2—Unverified
6ALIGNAccuracy96.13—Unverified
7SR-GNNAccuracy96.1—Unverified
8EffNet-L2 (SAM)Accuracy95.96—Unverified
9SaSPA + CALAccuracy95.72—Unverified
10CAPAccuracy95.7—Unverified
#ModelMetricClaimedVerifiedStatus
1I2-HOFIAccuracy96.42—Unverified
2SR-GNNAccuracy95.4—Unverified
3Inceptionv4Accuracy95.11—Unverified
4CAPAccuracy94.9—Unverified
5CSQA-NetAccuracy94.7—Unverified
6CMAL-NetAccuracy94.7—Unverified
7TBMSL-NetAccuracy94.7—Unverified
8PARTAccuracy94.6—Unverified
9SaSPA + CALAccuracy94.5—Unverified
10AENetAccuracy94.5—Unverified
#ModelMetricClaimedVerifiedStatus
1HERBSAccuracy93.1—Unverified
2PIMAccuracy92.8—Unverified
3MDCMAccuracy92.5—Unverified
4IELTAccuracy91.8—Unverified
5CAPAccuracy91.8—Unverified
6SFETransAccuracy91.8—Unverified
7SWAG (ViT H/14)Accuracy91.7—Unverified
8ViT-NeTAccuracy91.7—Unverified
9TransFGAccuracy91.7—Unverified
10I2-HOFIAccuracy91.6—Unverified
#ModelMetricClaimedVerifiedStatus
1HERBSAccuracy93—Unverified
2MetaFormer (MetaFormer-2,384)Accuracy93—Unverified
3PIMAccuracy92.8—Unverified
4MPSAAccuracy92.5—Unverified
5ViT-NeT (SwinV2-B)Accuracy92.5—Unverified
6CSQA-NetAccuracy92.3—Unverified
7I2-HOFIAccuracy92.12—Unverified
8MDCMAccuracy92—Unverified
9CGLAccuracy91.7—Unverified
10SR-GNNAccuracy91.2—Unverified