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 151200 of 353 papers

TitleStatusHype
Human Attention in Fine-grained ClassificationCode1
EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification TaskCode0
The Aircraft Context Dataset: Understanding and Optimizing Data Variability in Aerial DomainsCode1
A Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species ClassificationCode1
Self-Supervised Learning by Estimating Twin Class DistributionsCode1
A free lunch from ViT:Adaptive Attention Multi-scale Fusion Transformer for Fine-grained Visual Recognition0
ResNet strikes back: An improved training procedure in timmCode1
Fine-Grained Few Shot Learning with Foreground Object Transformation0
Dead Pixel Test Using Effective Receptive FieldCode0
Object-aware Long-short-range Spatial Alignment for Few-Shot Fine-Grained Image Classification0
Towards Fine-grained Image Classification with Generative Adversarial Networks and Facial Landmark DetectionCode1
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identificationCode1
TDLS: A Top-Down Layer Searching Algorithm for Generating Counterfactual Visual Explanation0
Self-Supervised Learning for Fine-Grained Image ClassificationCode1
Rethinking Hard-Parameter Sharing in Multi-Domain Learning0
Non-binary deep transfer learning for image classificationCode1
RAMS-Trans: Recurrent Attention Multi-scale Transformer forFine-grained Image Recognition0
Transformer with Peak Suppression and Knowledge Guidance for Fine-grained Image Recognition0
Feature Fusion Vision Transformer for Fine-Grained Visual CategorizationCode1
AutoFormer: Searching Transformers for Visual RecognitionCode2
Exploring Localization for Self-supervised Fine-grained Contrastive LearningCode0
The Hitchhiker's Guide to Prior-Shift AdaptationCode0
Cross-layer Navigation Convolutional Neural Network for Fine-grained Visual Classification0
Learning Deep Classifiers Consistent With Fine-Grained Novelty Detection0
Graph-Based High-Order Relation Discovery for Fine-Grained Recognition0
Exploring Vision Transformers for Fine-grained ClassificationCode1
NDPNet: A novel non-linear data projection network for few-shot fine-grained image classification0
The 2021 Hotel-ID to Combat Human Trafficking Competition Dataset0
Channel DropBlock: An Improved Regularization Method for Fine-Grained Visual Classification0
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
ResMLP: Feedforward networks for image classification with data-efficient trainingCode1
With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual RepresentationsCode0
ImageNet-21K Pretraining for the MassesCode1
CA-PMG: Channel attention and progressive multi-granularity training network for fine-grained visual classification0
Escaping the Big Data Paradigm with Compact TransformersCode1
Streaming Self-Training via Domain-Agnostic Unlabeled Images0
ProgressiveSpinalNet architecture for FC layersCode0
Danish Fungi 2020 -- Not Just Another Image Recognition DatasetCode1
TransFG: A Transformer Architecture for Fine-grained RecognitionCode1
Cut-Thumbnail: A Novel Data Augmentation for Convolutional Neural NetworkCode0
Interpretable Attention Guided Network for Fine-grained Visual Classification0
Learning Granularity-Aware Convolutional Neural Network for Fine-Grained Visual Classification0
Feature Boosting, Suppression, and Diversification for Fine-Grained Visual ClassificationCode1
Alignment Enhancement Network for Fine-grained Visual Categorization0
Transformer in TransformerCode1
Re-rank Coarse Classification with Local Region Enhanced Features for Fine-Grained Image Recognition0
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text SupervisionCode2
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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
5CMAL-NetAccuracy94.7Unverified
6TBMSL-NetAccuracy94.7Unverified
7CSQA-NetAccuracy94.7Unverified
8PARTAccuracy94.6Unverified
9AENetAccuracy94.5Unverified
10SaSPA + CALAccuracy94.5Unverified
#ModelMetricClaimedVerifiedStatus
1HERBSAccuracy93.1Unverified
2PIMAccuracy92.8Unverified
3MDCMAccuracy92.5Unverified
4SFETransAccuracy91.8Unverified
5CAPAccuracy91.8Unverified
6IELTAccuracy91.8Unverified
7TransFGAccuracy91.7Unverified
8SWAG (ViT H/14)Accuracy91.7Unverified
9ViT-NeTAccuracy91.7Unverified
10FFVTAccuracy91.6Unverified
#ModelMetricClaimedVerifiedStatus
1HERBSAccuracy93Unverified
2MetaFormer (MetaFormer-2,384)Accuracy93Unverified
3PIMAccuracy92.8Unverified
4ViT-NeT (SwinV2-B)Accuracy92.5Unverified
5MPSAAccuracy92.5Unverified
6CSQA-NetAccuracy92.3Unverified
7I2-HOFIAccuracy92.12Unverified
8MDCMAccuracy92Unverified
9CGLAccuracy91.7Unverified
10SR-GNNAccuracy91.2Unverified