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 201–250 of 353 papers

TitleStatusHype
Automatic Fine-grained Glomerular Lesion Recognition in Kidney Pathology—0
Ensembles of Vision Transformers as a New Paradigm for Automated Classification in EcologyCode0
Bridge the Gap between Supervised and Unsupervised Learning for Fine-Grained Classification—0
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision—0
Cross-Part Learning for Fine-Grained Image ClassificationCode0
Progressive Multi-stage Interactive Training in Mobile Network for Fine-grained Recognition—0
Improved Robustness of Vision Transformer via PreLayerNorm in Patch Embedding—0
High-Order-Interaction for weakly supervised Fine-Grained Visual CategorizationCode0
EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification TaskCode0
A free lunch from ViT:Adaptive Attention Multi-scale Fusion Transformer for Fine-grained Visual Recognition—0
Fine-Grained Few Shot Learning with Foreground Object Transformation—0
Dead Pixel Test Using Effective Receptive FieldCode0
Object-aware Long-short-range Spatial Alignment for Few-Shot Fine-Grained Image Classification—0
TDLS: A Top-Down Layer Searching Algorithm for Generating Counterfactual Visual Explanation—0
Rethinking Hard-Parameter Sharing in Multi-Domain Learning—0
RAMS-Trans: Recurrent Attention Multi-scale Transformer forFine-grained Image Recognition—0
Transformer with Peak Suppression and Knowledge Guidance for Fine-grained Image Recognition—0
Exploring Localization for Self-supervised Fine-grained Contrastive Learning—0
The Hitchhiker's Guide to Prior-Shift AdaptationCode0
Cross-layer Navigation Convolutional Neural Network for Fine-grained Visual Classification—0
Graph-Based High-Order Relation Discovery for Fine-Grained Recognition—0
Learning Deep Classifiers Consistent With Fine-Grained Novelty Detection—0
NDPNet: A novel non-linear data projection network for few-shot fine-grained image classification—0
The 2021 Hotel-ID to Combat Human Trafficking Competition Dataset—0
Channel DropBlock: An Improved Regularization Method for Fine-Grained Visual Classification—0
BR-NPA: A Non-Parametric High-Resolution Attention Model to improve the Interpretability of AttentionCode0
Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of AttentionCode0
When Vision Transformers Outperform ResNets without Pre-training or Strong Data AugmentationsCode0
When Does Contrastive Visual Representation Learning Work?—0
With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual RepresentationsCode0
CA-PMG: Channel attention and progressive multi-granularity training network for fine-grained visual classification—0
Streaming Self-Training via Domain-Agnostic Unlabeled Images—0
ProgressiveSpinalNet architecture for FC layersCode0
Cut-Thumbnail: A Novel Data Augmentation for Convolutional Neural NetworkCode0
Interpretable Attention Guided Network for Fine-grained Visual Classification—0
Learning Granularity-Aware Convolutional Neural Network for Fine-Grained Visual Classification—0
Alignment Enhancement Network for Fine-grained Visual Categorization—0
Re-rank Coarse Classification with Local Region Enhanced Features for Fine-Grained Image Recognition—0
Grad-CAM guided channel-spatial attention module for fine-grained visual classification—0
Auto-view contrastive learning for few-shot image recognition—0
Exploring Target Driven Image Classification—0
A Unified Framework to Analyze and Design the Nonlocal Blocks for Neural Networks—0
Natural World Distribution via Adaptive Confusion Energy Regularization—0
Knowledge Transfer Based Fine-grained Visual ClassificationCode0
Assessing The Importance Of Colours For CNNs In Object Recognition—0
Fine-grained Classification via Categorical Memory Networks—0
Grafit: Learning fine-grained image representations with coarse labels—0
Learning Class Unique Features in Fine-Grained Visual Classification—0
Beyond the Attention: Distinguish the Discriminative and Confusable Features For Fine-grained Image Classification—0
Domain Adaptive Transfer Learning on Visual Attention Aware Data Augmentation for Fine-grained Visual Categorization—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
5CSQA-NetAccuracy94.7—Unverified
6CMAL-NetAccuracy94.7—Unverified
7TBMSL-NetAccuracy94.7—Unverified
8PARTAccuracy94.6—Unverified
9SaSPA + CALAccuracy94.5—Unverified
10AENetAccuracy94.5—Unverified
#ModelMetricClaimedVerifiedStatus
1HERBSAccuracy93.1—Unverified
2PIMAccuracy92.8—Unverified
3MDCMAccuracy92.5—Unverified
4IELTAccuracy91.8—Unverified
5CAPAccuracy91.8—Unverified
6SFETransAccuracy91.8—Unverified
7TransFGAccuracy91.7—Unverified
8ViT-NeTAccuracy91.7—Unverified
9SWAG (ViT H/14)Accuracy91.7—Unverified
10FFVTAccuracy91.6—Unverified
#ModelMetricClaimedVerifiedStatus
1MetaFormer (MetaFormer-2,384)Accuracy93—Unverified
2HERBSAccuracy93—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