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

Showing 1–50 of 353 papers

TitleStatusHype
Visual-RFT: Visual Reinforcement Fine-TuningCode7
DINOv2: Learning Robust Visual Features without SupervisionCode6
AutoAugment: Learning Augmentation Policies from DataCode3
EfficientNet: Rethinking Model Scaling for Convolutional Neural NetworksCode3
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text SupervisionCode2
A Simple Episodic Linear Probe Improves Visual Recognition in the WildCode2
DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTsCode2
Your Diffusion Model is Secretly a Zero-Shot ClassifierCode2
AutoFormer: Searching Transformers for Visual RecognitionCode2
MetaFormer: A Unified Meta Framework for Fine-Grained RecognitionCode2
A Novel Plug-in Module for Fine-Grained Visual ClassificationCode2
Big Transfer (BiT): General Visual Representation LearningCode2
Prompt-CAM: A Simpler Interpretable Transformer for Fine-Grained AnalysisCode2
GPipe: Efficient Training of Giant Neural Networks using Pipeline ParallelismCode2
Fixing the train-test resolution discrepancyCode2
Sharpness-Aware Minimization for Efficiently Improving GeneralizationCode2
Diffusion Models Beat GANs on Image ClassificationCode1
Gradient Centralization: A New Optimization Technique for Deep Neural NetworksCode1
Generative Parameter-Efficient Fine-TuningCode1
Focus Longer to See Better:Recursively Refined Attention for Fine-Grained Image ClassificationCode1
Good Questions Help Zero-Shot Image ReasoningCode1
Human Attention in Fine-grained ClassificationCode1
Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNetsCode1
Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural NetworkCode1
DenoiseRep: Denoising Model for Representation LearningCode1
GIST: Generating Image-Specific Text for Fine-grained Object ClassificationCode1
Destruction and Construction Learning for Fine-Grained Image RecognitionCode1
Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw PatchesCode1
Fine-Grained Predicates Learning for Scene Graph GenerationCode1
Fine-Grained Visual Classification via Simultaneously Learning of Multi-regional Multi-grained FeaturesCode1
Feature Fusion Vision Transformer for Fine-Grained Visual CategorizationCode1
Danish Fungi 2020 -- Not Just Another Image Recognition DatasetCode1
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual FeaturesCode1
Fine-Grained Visual Classification via Internal Ensemble Learning TransformerCode1
BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and MappingCode1
Exploration of Class Center for Fine-Grained Visual ClassificationCode1
A Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species ClassificationCode1
Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image ClassificationCode1
Are These Birds Similar: Learning Branched Networks for Fine-grained RepresentationsCode1
BSNet: Bi-Similarity Network for Few-shot Fine-grained Image ClassificationCode1
Contrastive Deep SupervisionCode1
Convolutional Fine-Grained Classification with Self-Supervised Target Relation RegularizationCode1
A Simple Interpretable Transformer for Fine-Grained Image Classification and AnalysisCode1
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identificationCode1
Clue Me In: Semi-Supervised FGVC with Out-of-Distribution DataCode1
Fine-Grained Visual Classification using Self Assessment ClassifierCode1
Exploring Vision Transformers for Fine-grained ClassificationCode1
Dataset Condensation with Contrastive SignalsCode1
Concept Learners for Few-Shot LearningCode1
Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine SynergyCode1
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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
7TransFGAccuracy91.7—Unverified
8ViT-NeTAccuracy91.7—Unverified
9SWAG (ViT H/14)Accuracy91.7—Unverified
10FFVTAccuracy91.6—Unverified
#ModelMetricClaimedVerifiedStatus
1MetaFormer (MetaFormer-2,384)Accuracy93—Unverified
2HERBSAccuracy93—Unverified
3PIMAccuracy92.8—Unverified
4ViT-NeT (SwinV2-B)Accuracy92.5—Unverified
5MPSAAccuracy92.5—Unverified
6CSQA-NetAccuracy92.3—Unverified
7I2-HOFIAccuracy92.12—Unverified
8MDCMAccuracy92—Unverified
9CGLAccuracy91.7—Unverified
10SR-GNNAccuracy91.2—Unverified