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 1–50 of 353 papers

TitleStatusHype
Hierarchical Mask-Enhanced Dual Reconstruction Network for Few-Shot Fine-Grained Image ClassificationCode0
Structural feature enhanced transformer for fine-grained image recognition—0
GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers—0
Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions—0
DS_FusionNet: Dynamic Dual-Stream Fusion with Bidirectional Knowledge Distillation for Plant Disease RecognitionCode0
Enhancing Multimodal In-Context Learning for Image Classification through Coreset Optimization—0
Cross-Hierarchical Bidirectional Consistency Learning for Fine-Grained Visual Classification—0
Adaptive Classification of Interval-Valued Time Series—0
Visual-RFT: Visual Reinforcement Fine-TuningCode7
An Attention-Locating Algorithm for Eliminating Background Effects in Fine-grained Visual ClassificationCode0
Prompt-CAM: A Simpler Interpretable Transformer for Fine-Grained AnalysisCode2
MedFocusCLIP : Improving few shot classification in medical datasets using pixel wise attention—0
Universal Fine-grained Visual Categorization by Concept Guided LearningCode0
Taxonomy-Aware Evaluation of Vision-Language Models—0
Enhancing Fine-grained Image Classification through Attentive Batch Training—0
SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation—0
Vision Mamba Distillation for Low-resolution Fine-grained Image ClassificationCode1
Interweaving Insights: High-Order Feature Interaction for Fine-Grained Visual RecognitionCode0
DivShift: Exploring Domain-Specific Distribution Shifts in Large-Scale, Volunteer-Collected Biodiversity DatasetsCode0
JPEG Inspired Deep LearningCode0
Stochastic Subsampling With Average Pooling—0
SIM-OFE: Structure Information Mining and Object-aware Feature Enhancement for Fine-Grained Visual Categorization—0
PEPL: Precision-Enhanced Pseudo-Labeling for Fine-Grained Image Classification in Semi-Supervised LearningCode0
Generating Binary Species Range MapsCode0
Extremely Fine-Grained Visual Classification over Resembling Glyphs in the WildCode0
Multi-Granularity Part Sampling Attention for Fine-Grained Visual ClassificationCode1
Orchid2024: A cultivar-level dataset and methodology for fine-grained classification of Chinese Cymbidium OrchidsCode0
Token Compensator: Altering Inference Cost of Vision Transformer without Re-TuningCode1
Exploration of Class Center for Fine-Grained Visual ClassificationCode1
PDiscoFormer: Relaxing Part Discovery Constraints with Vision TransformersCode1
Hybrid Feature Collaborative Reconstruction Network for Few-Shot Fine-Grained Image Classification—0
Extract More from Less: Efficient Fine-Grained Visual Recognition in Low-Data RegimesCode0
Advancing Fine-Grained Classification by Structure and Subject Preserving AugmentationCode1
DenoiseRep: Denoising Model for Representation LearningCode1
Few-Shot Classification of Interactive Activities of Daily Living (InteractADL)Code0
LLM-based Hierarchical Concept Decomposition for Interpretable Fine-Grained Image Classification—0
Enhancing Fine-Grained Image Classifications via Cascaded Vision Language Models—0
How Quality Affects Deep Neural Networks in Fine-Grained Image Classification—0
Robust and Explainable Fine-Grained Visual Classification with Transfer Learning: A Dual-Carriageway Framework—0
DCNN: Dual Cross-current Neural Networks Realized Using An Interactive Deep Learning Discriminator for Fine-grained Objects—0
DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTsCode2
Context-Semantic Quality Awareness Network for Fine-Grained Visual Categorization—0
Premonition: Using Generative Models to Preempt Future Data Changes in Continual LearningCode0
Spurious Feature Eraser: Stabilizing Test-Time Adaptation for Vision-Language Foundation ModelCode0
Deep Neural Network Models Trained With A Fixed Random Classifier Transfer Better Across Domains—0
Progressive Multi-task Anti-Noise Learning and Distilling Frameworks for Fine-grained Vehicle RecognitionCode1
Improved Zero-Shot Classification by Adapting VLMs with Text DescriptionsCode1
OmniVec2 - A Novel Transformer based Network for Large Scale Multimodal and Multitask Learning—0
Morphing Tokens Draw Strong Masked Image ModelsCode0
Roll With the Punches: Expansion and Shrinkage of Soft Label Selection for Semi-supervised Fine-Grained LearningCode1
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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
5CMAL-NetAccuracy94.7—Unverified
6TBMSL-NetAccuracy94.7—Unverified
7CSQA-NetAccuracy94.7—Unverified
8PARTAccuracy94.6—Unverified
9AENetAccuracy94.5—Unverified
10SaSPA + CALAccuracy94.5—Unverified
#ModelMetricClaimedVerifiedStatus
1HERBSAccuracy93.1—Unverified
2PIMAccuracy92.8—Unverified
3MDCMAccuracy92.5—Unverified
4SFETransAccuracy91.8—Unverified
5CAPAccuracy91.8—Unverified
6IELTAccuracy91.8—Unverified
7TransFGAccuracy91.7—Unverified
8SWAG (ViT H/14)Accuracy91.7—Unverified
9ViT-NeTAccuracy91.7—Unverified
10FFVTAccuracy91.6—Unverified
#ModelMetricClaimedVerifiedStatus
1HERBSAccuracy93—Unverified
2MetaFormer (MetaFormer-2,384)Accuracy93—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