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 151–200 of 353 papers

TitleStatusHype
DCNN: Dual Cross-current Neural Networks Realized Using An Interactive Deep Learning Discriminator for Fine-grained Objects—0
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
OmniVec2 - A Novel Transformer based Network for Large Scale Multimodal and Multitask Learning—0
Morphing Tokens Draw Strong Masked Image ModelsCode0
Human-in-the-Loop Visual Re-ID for Population Size EstimationCode0
OmniVec: Learning robust representations with cross modal sharing—0
Dining on Details: LLM-Guided Expert Networks for Fine-Grained Food Recognition—0
Gramian Attention Heads are Strong yet Efficient Vision LearnersCode0
Delving into Multimodal Prompting for Fine-grained Visual Classification—0
PCNN: Probable-Class Nearest-Neighbor Explanations Improve Fine-Grained Image Classification Accuracy for AIs and HumansCode0
Multiscale patch-based feature graphs for image classificationCode0
Deep Neural Networks Fused with Textures for Image Classification—0
Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis—0
Semantically-Prompted Language Models Improve Visual Descriptions—0
Feature Channel Adaptive Enhancement for Fine-Grained Visual Classification—0
Understanding Gaussian Attention Bias of Vision Transformers Using Effective Receptive FieldsCode0
Leaf Cultivar Identification via Prototype-enhanced Learning—0
PVP: Pre-trained Visual Parameter-Efficient Tuning—0
Cascading Hierarchical Networks with Multi-task Balanced Loss for Fine-grained hashingCode0
Semantic Feature Integration network for Fine-grained Visual Classification—0
How to Use Dropout Correctly on Residual Networks with Batch NormalizationCode0
LiT Tuned Models for Efficient Species DetectionCode0
On the Ideal Number of Groups for Isometric Gradient Propagation—0
An Erudite Fine-Grained Visual Classification Model—0
Multi-View Active Fine-Grained Visual RecognitionCode0
TransIFC: Invariant Cues-aware Feature Concentration Learning for Efficient Fine-grained Bird Image Classification—0
Data Augmentation Vision Transformer for Fine-grained Image Classification—0
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
Conviformers: Convolutionally guided Vision TransformerCode0
Preserving Fine-Grain Feature Information in Classification via Entropic RegularizationCode0
Explored An Effective Methodology for Fine-Grained Snake RecognitionCode0
Few-shot Fine-grained Image Classification via Multi-Frequency Neighborhood and Double-cross ModulationCode0
Adaptive Fine-Grained Predicates Learning for Scene Graph Generation—0
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 Recognition—0
An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning SystemsCode0
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
ViT-FOD: A Vision Transformer based Fine-grained Object Discriminator—0
Three things everyone should know about Vision TransformersCode0
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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