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 251–300 of 353 papers

TitleStatusHype
Part-Stacked CNN for Fine-Grained Visual Categorization—0
Pay Attention to Convolution Filters: Towards Fast and Accurate Fine-Grained Transfer Learning—0
Fast Fine-grained Image Classification via Weakly Supervised Discriminative Localization—0
Exploring Target Driven Image Classification—0
Semantically-Prompted Language Models Improve Visual Descriptions—0
Performing Image Classification for 10 Different Monkey Species using CNN—0
A free lunch from ViT:Adaptive Attention Multi-scale Fusion Transformer for Fine-grained Visual Recognition—0
Enhancing Multimodal In-Context Learning for Image Classification through Coreset Optimization—0
ViT-FOD: A Vision Transformer based Fine-grained Object Discriminator—0
Enhancing Fine-Grained Image Classifications via Cascaded Vision Language Models—0
Progressive Multi-stage Interactive Training in Mobile Network for Fine-grained Recognition—0
Enhancing Fine-grained Image Classification through Attentive Batch Training—0
Adaptive Fine-Grained Predicates Learning for Scene Graph Generation—0
Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors—0
Embedding Label Structures for Fine-Grained Feature Representation—0
PVP: Pre-trained Visual Parameter-Efficient Tuning—0
RAMS-Trans: Recurrent Attention Multi-scale Transformer forFine-grained Image Recognition—0
ReDro: Efficiently Learning Large-sized SPD Visual Representation—0
Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification—0
Domain Adaptive Transfer Learning with Specialist Models—0
Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition—0
Re-rank Coarse Classification with Local Region Enhanced Features for Fine-Grained Image Recognition—0
Domain Adaptive Transfer Learning on Visual Attention Aware Data Augmentation for Fine-grained Visual Categorization—0
Do Better ImageNet Models Transfer Better?—0
Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patches—0
Rethinking Hard-Parameter Sharing in Multi-Domain Learning—0
Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification—0
Dining on Details: LLM-Guided Expert Networks for Fine-Grained Food Recognition—0
Robust and Explainable Fine-Grained Visual Classification with Transfer Learning: A Dual-Carriageway Framework—0
Detecting Visually Relevant Sentences for Fine-Grained Classification—0
RP2K: A Large-Scale Retail Product Dataset for Fine-Grained Image Classification—0
Delving into Multimodal Prompting for Fine-grained Visual Classification—0
Deformable Part Descriptors for Fine-grained Recognition and Attribute Prediction—0
Deep Quantization: Encoding Convolutional Activations with Deep Generative Model—0
Deep Neural Networks Fused with Textures for Image Classification—0
Weakly Supervised Bilinear Attention Network for Fine-Grained Visual Classification—0
Deep Neural Network Models Trained With A Fixed Random Classifier Transfer Better Across Domains—0
DCNN: Dual Cross-current Neural Networks Realized Using An Interactive Deep Learning Discriminator for Fine-grained Objects—0
Semantic Feature Integration network for Fine-grained Visual Classification—0
SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation—0
Data Augmentation Vision Transformer for Fine-grained Image Classification—0
Siamese Networks: The Tale of Two Manifolds—0
SIM-OFE: Structure Information Mining and Object-aware Feature Enhancement for Fine-Grained Visual Categorization—0
DAF-NET: a saliency based weakly supervised method of dual attention fusion for fine-grained image classification—0
Cross-layer Navigation Convolutional Neural Network for Fine-grained Visual Classification—0
Spatial-Aware Non-Local Attention for Fashion Landmark Detection—0
Cross-Hierarchical Bidirectional Consistency Learning for Fine-Grained Visual Classification—0
Convolutional Low-Resolution Fine-Grained Classification—0
Stochastic Subsampling With Average Pooling—0
Streaming Self-Training via Domain-Agnostic Unlabeled Images—0
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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