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 150 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
Fixing the train-test resolution discrepancyCode2
A Simple Episodic Linear Probe Improves Visual Recognition in the WildCode2
Your Diffusion Model is Secretly a Zero-Shot ClassifierCode2
DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTsCode2
AutoFormer: Searching Transformers for Visual RecognitionCode2
GPipe: Efficient Training of Giant Neural Networks using Pipeline ParallelismCode2
A Novel Plug-in Module for Fine-Grained Visual ClassificationCode2
MetaFormer: A Unified Meta Framework for Fine-Grained RecognitionCode2
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text SupervisionCode2
Big Transfer (BiT): General Visual Representation LearningCode2
Prompt-CAM: A Simpler Interpretable Transformer for Fine-Grained AnalysisCode2
Sharpness-Aware Minimization for Efficiently Improving GeneralizationCode2
Good Questions Help Zero-Shot Image ReasoningCode1
Generative Parameter-Efficient Fine-TuningCode1
Gradient Centralization: A New Optimization Technique for Deep Neural NetworksCode1
Fine-Grained Visual Classification via Internal Ensemble Learning TransformerCode1
Dataset Condensation with Contrastive SignalsCode1
Fine-Grained Visual Classification via Simultaneously Learning of Multi-regional Multi-grained FeaturesCode1
Focus Longer to See Better:Recursively Refined Attention for Fine-Grained Image ClassificationCode1
Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw PatchesCode1
Human Attention in Fine-grained ClassificationCode1
Fine-grained Visual Classification with High-temperature Refinement and Background SuppressionCode1
GIST: Generating Image-Specific Text for Fine-grained Object ClassificationCode1
Feature Fusion Vision Transformer for Fine-Grained Visual CategorizationCode1
Exploring Vision Transformers for Fine-grained ClassificationCode1
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual FeaturesCode1
Escaping the Big Data Paradigm with Compact TransformersCode1
Contrastive Deep SupervisionCode1
Exploration of Class Center for Fine-Grained Visual ClassificationCode1
Fine-Grained Predicates Learning for Scene Graph GenerationCode1
BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and MappingCode1
Context-aware Attentional Pooling (CAP) 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
Diffusion Models Beat GANs on Image ClassificationCode1
DenoiseRep: Denoising Model for Representation LearningCode1
A Simple Interpretable Transformer for Fine-Grained Image Classification and AnalysisCode1
Convolutional Fine-Grained Classification with Self-Supervised Target Relation RegularizationCode1
Clue Me In: Semi-Supervised FGVC with Out-of-Distribution DataCode1
Feature Boosting, Suppression, and Diversification for Fine-Grained Visual ClassificationCode1
Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNetsCode1
Danish Fungi 2020 -- Not Just Another Image Recognition DatasetCode1
Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural NetworkCode1
Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine SynergyCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TResnet-L + PMDAccuracy97.3Unverified
2CMAL-NetAccuracy97.1Unverified
3I2-HOFIAccuracy96.92Unverified
4TResNet-L + ML-DecoderAccuracy96.41Unverified
5DATAccuracy96.2Unverified
6ALIGNAccuracy96.13Unverified
7SR-GNNAccuracy96.1Unverified
8EffNet-L2 (SAM)Accuracy95.96Unverified
9SaSPA + CALAccuracy95.72Unverified
10CAPAccuracy95.7Unverified
#ModelMetricClaimedVerifiedStatus
1I2-HOFIAccuracy96.42Unverified
2SR-GNNAccuracy95.4Unverified
3Inceptionv4Accuracy95.11Unverified
4CAPAccuracy94.9Unverified
5CMAL-NetAccuracy94.7Unverified
6TBMSL-NetAccuracy94.7Unverified
7CSQA-NetAccuracy94.7Unverified
8PARTAccuracy94.6Unverified
9AENetAccuracy94.5Unverified
10SaSPA + CALAccuracy94.5Unverified
#ModelMetricClaimedVerifiedStatus
1HERBSAccuracy93.1Unverified
2PIMAccuracy92.8Unverified
3MDCMAccuracy92.5Unverified
4SFETransAccuracy91.8Unverified
5CAPAccuracy91.8Unverified
6IELTAccuracy91.8Unverified
7TransFGAccuracy91.7Unverified
8SWAG (ViT H/14)Accuracy91.7Unverified
9ViT-NeTAccuracy91.7Unverified
10FFVTAccuracy91.6Unverified
#ModelMetricClaimedVerifiedStatus
1HERBSAccuracy93Unverified
2MetaFormer (MetaFormer-2,384)Accuracy93Unverified
3PIMAccuracy92.8Unverified
4ViT-NeT (SwinV2-B)Accuracy92.5Unverified
5MPSAAccuracy92.5Unverified
6CSQA-NetAccuracy92.3Unverified
7I2-HOFIAccuracy92.12Unverified
8MDCMAccuracy92Unverified
9CGLAccuracy91.7Unverified
10SR-GNNAccuracy91.2Unverified