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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–200 of 353 papers

TitleStatusHype
Evaluation of Output Embeddings for Fine-Grained Image ClassificationCode0
Explored An Effective Methodology for Fine-Grained Snake RecognitionCode0
Extract More from Less: Efficient Fine-Grained Visual Recognition in Low-Data RegimesCode0
Extremely Fine-Grained Visual Classification over Resembling Glyphs in the WildCode0
Few-Shot Classification of Interactive Activities of Daily Living (InteractADL)Code0
Few-shot Fine-grained Image Classification via Multi-Frequency Neighborhood and Double-cross ModulationCode0
Enhancing Fine-Grained 3D Object Recognition using Hybrid Multi-Modal Vision Transformer-CNN ModelsCode0
Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic EmbeddingCode0
Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of AttentionCode0
Generating Binary Species Range MapsCode0
Geo-Aware Networks for Fine-Grained RecognitionCode0
Gramian Attention Heads are Strong yet Efficient Vision LearnersCode0
Hierarchical Mask-Enhanced Dual Reconstruction Network for Few-Shot Fine-Grained Image ClassificationCode0
High-Order-Interaction for weakly supervised Fine-Grained Visual CategorizationCode0
How to Use Dropout Correctly on Residual Networks with Batch NormalizationCode0
Human-in-the-Loop Visual Re-ID for Population Size EstimationCode0
BR-NPA: A Non-Parametric High-Resolution Attention Model to improve the Interpretability of AttentionCode0
Interweaving Insights: High-Order Feature Interaction for Fine-Grained Visual RecognitionCode0
Spurious Feature Eraser: Stabilizing Test-Time Adaptation for Vision-Language Foundation ModelCode0
IP102: A Large-Scale Benchmark Dataset for Insect Pest RecognitionCode0
JPEG Inspired Deep LearningCode0
Knowledge Transfer Based Fine-grained Visual ClassificationCode0
Learning a Mixture of Granularity-Specific Experts for Fine-Grained CategorizationCode0
Learning Multi-Attention Convolutional Neural Network for Fine-Grained Image RecognitionCode0
Learning Multi-Subset of Classes for Fine-Grained Food RecognitionCode0
LiT Tuned Models for Efficient Species DetectionCode0
Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image RecognitionCode0
Morphing Tokens Draw Strong Masked Image ModelsCode0
Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual RecognitionCode0
Multiscale patch-based feature graphs for image classificationCode0
Multi-View Active Fine-Grained RecognitionCode0
Multi-View Active Fine-Grained Visual RecognitionCode0
Object-Part Attention Model for Fine-grained Image ClassificationCode0
On the Eigenvalues of Global Covariance Pooling for Fine-grained Visual RecognitionCode0
Orchid2024: A cultivar-level dataset and methodology for fine-grained classification of Chinese Cymbidium OrchidsCode0
Pairwise Confusion for Fine-Grained Visual ClassificationCode0
Part-Guided Attention Learning for Vehicle Instance RetrievalCode0
PEPL: Precision-Enhanced Pseudo-Labeling for Fine-Grained Image Classification in Semi-Supervised LearningCode0
Premonition: Using Generative Models to Preempt Future Data Changes in Continual LearningCode0
Preserving Fine-Grain Feature Information in Classification via Entropic RegularizationCode0
ProgressiveSpinalNet architecture for FC layersCode0
Rethinking Softmax with Cross-Entropy: Neural Network Classifier as Mutual Information EstimatorCode0
Selective Sparse Sampling for Fine-Grained Image RecognitionCode0
The Hitchhiker's Guide to Prior-Shift AdaptationCode0
The Unreasonable Effectiveness of Noisy Data for Fine-Grained RecognitionCode0
Three things everyone should know about Vision TransformersCode0
Cut-Thumbnail: A Novel Data Augmentation for Convolutional Neural NetworkCode0
Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root NormalizationCode0
Understanding Gaussian Attention Bias of Vision Transformers Using Effective Receptive FieldsCode0
Universal Fine-grained Visual Categorization by Concept Guided LearningCode0
Show:102550
← PrevPage 4 of 8Next →
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