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 51100 of 353 papers

TitleStatusHype
2nd Place Solution to Google Universal Image EmbeddingCode1
SR-GNN: Spatial Relation-aware Graph Neural Network for Fine-Grained Image CategorizationCode1
SIM-Trans: Structure Information Modeling Transformer for Fine-grained Visual CategorizationCode1
Convolutional Fine-Grained Classification with Self-Supervised Target Relation RegularizationCode1
Visual correspondence-based explanations improve AI robustness and human-AI team accuracyCode1
ViT-NeT: Interpretable Vision Transformers with Neural Tree DecoderCode1
Contrastive Deep SupervisionCode1
Learning Cross-Image Object Semantic Relation in Transformer for Few-Shot Fine-Grained Image ClassificationCode1
Fine-Grained Visual Classification using Self Assessment ClassifierCode1
Fine-Grained Predicates Learning for Scene Graph GenerationCode1
Knowledge Mining with Scene Text for Fine-Grained RecognitionCode1
Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine SynergyCode1
Dynamic MLP for Fine-Grained Image Classification by Leveraging Geographical and Temporal InformationCode1
Image Difference Captioning with Pre-training and Contrastive LearningCode1
Dataset Condensation with Contrastive SignalsCode1
Revisiting Weakly Supervised Pre-Training of Visual Perception ModelsCode1
Label Relation Graphs Enhanced Hierarchical Residual Network for Hierarchical Multi-Granularity ClassificationCode1
Clue Me In: Semi-Supervised FGVC with Out-of-Distribution DataCode1
Making a Bird AI Expert Work for You and MeCode1
ML-Decoder: Scalable and Versatile Classification HeadCode1
Human Attention in Fine-grained ClassificationCode1
The Aircraft Context Dataset: Understanding and Optimizing Data Variability in Aerial DomainsCode1
Self-Supervised Learning by Estimating Twin Class DistributionsCode1
A Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species ClassificationCode1
ResNet strikes back: An improved training procedure in timmCode1
Towards Fine-grained Image Classification with Generative Adversarial Networks and Facial Landmark DetectionCode1
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identificationCode1
Self-Supervised Learning for Fine-Grained Image ClassificationCode1
Non-binary deep transfer learning for image classificationCode1
Feature Fusion Vision Transformer for Fine-Grained Visual CategorizationCode1
Exploring Vision Transformers for Fine-grained ClassificationCode1
ResMLP: Feedforward networks for image classification with data-efficient trainingCode1
ImageNet-21K Pretraining for the MassesCode1
Escaping the Big Data Paradigm with Compact TransformersCode1
Danish Fungi 2020 -- Not Just Another Image Recognition DatasetCode1
TransFG: A Transformer Architecture for Fine-grained RecognitionCode1
Feature Boosting, Suppression, and Diversification for Fine-Grained Visual ClassificationCode1
Transformer in TransformerCode1
Fine-Grained Visual Classification via Simultaneously Learning of Multi-regional Multi-grained FeaturesCode1
Progressive Co-Attention Network for Fine-grained Visual ClassificationCode1
Context-aware Attentional Pooling (CAP) for Fine-grained Visual ClassificationCode1
Training data-efficient image transformers & distillation through attentionCode1
SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained DataCode1
BSNet: Bi-Similarity Network for Few-shot Fine-grained Image ClassificationCode1
Your "Flamingo" is My "Bird": Fine-Grained, or NotCode1
Multi-Modal Reasoning Graph for Scene-Text Based Fine-Grained Image Classification and RetrievalCode1
Concept Learners for Few-Shot LearningCode1
SpinalNet: Deep Neural Network with Gradual InputCode1
ULSAM: Ultra-Lightweight Subspace Attention Module for Compact Convolutional Neural NetworksCode1
Learning Semantically Enhanced Feature for Fine-Grained Image ClassificationCode1
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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
5CSQA-NetAccuracy94.7Unverified
6CMAL-NetAccuracy94.7Unverified
7TBMSL-NetAccuracy94.7Unverified
8PARTAccuracy94.6Unverified
9SaSPA + CALAccuracy94.5Unverified
10AENetAccuracy94.5Unverified
#ModelMetricClaimedVerifiedStatus
1HERBSAccuracy93.1Unverified
2PIMAccuracy92.8Unverified
3MDCMAccuracy92.5Unverified
4IELTAccuracy91.8Unverified
5CAPAccuracy91.8Unverified
6SFETransAccuracy91.8Unverified
7SWAG (ViT H/14)Accuracy91.7Unverified
8ViT-NeTAccuracy91.7Unverified
9TransFGAccuracy91.7Unverified
10I2-HOFIAccuracy91.6Unverified
#ModelMetricClaimedVerifiedStatus
1HERBSAccuracy93Unverified
2MetaFormer (MetaFormer-2,384)Accuracy93Unverified
3PIMAccuracy92.8Unverified
4MPSAAccuracy92.5Unverified
5ViT-NeT (SwinV2-B)Accuracy92.5Unverified
6CSQA-NetAccuracy92.3Unverified
7I2-HOFIAccuracy92.12Unverified
8MDCMAccuracy92Unverified
9CGLAccuracy91.7Unverified
10SR-GNNAccuracy91.2Unverified