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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 101–125 of 353 papers

Title	Date	Tasks	Status	Hype
MergedNET: A simple approach for one-shot learning in siamese networks based on similarity layers	Oct 14, 2022	Few-Shot Image ClassificationFine-Grained Image Classification	CodeCode Available	0
Learning Multi-Subset of Classes for Fine-Grained Food Recognition	Oct 10, 2022	Fine-Grained Image ClassificationFood Recognition	CodeCode Available	0
Enhancing Fine-Grained 3D Object Recognition using Hybrid Multi-Modal Vision Transformer-CNN Models	Oct 3, 2022	3D Object RecognitionFine-Grained Image Classification	CodeCode Available	0
Fine-grained Classification of Solder Joints with α-skew Jensen-Shannon Divergence	Sep 20, 2022	ClassificationFine-Grained Image Classification	—Unverified	0
A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems	Sep 15, 2022	Domain GeneralizationFine-Grained Image Classification	CodeCode Available	0
Bag of Tricks and a Strong Baseline for FGVC	Sep 5, 2022	ClassificationFine-Grained Image Classification	CodeCode Available	0
SR-GNN: Spatial Relation-aware Graph Neural Network for Fine-Grained Image Categorization	Sep 5, 2022	Fine-Grained Image ClassificationGraph Neural Network	CodeCode Available	1
SIM-Trans: Structure Information Modeling Transformer for Fine-grained Visual Categorization	Aug 31, 2022	Contrastive LearningFine-Grained Image Classification	CodeCode Available	1
Conviformers: Convolutionally guided Vision Transformer	Aug 17, 2022	Fine-Grained Image Classificationimage-classification	CodeCode Available	0
Preserving Fine-Grain Feature Information in Classification via Entropic Regularization	Aug 7, 2022	ClassificationDiversity	CodeCode Available	0
Convolutional Fine-Grained Classification with Self-Supervised Target Relation Regularization	Aug 3, 2022	ClassificationData Augmentation	CodeCode Available	1
Visual correspondence-based explanations improve AI robustness and human-AI team accuracy	Jul 26, 2022	Adversarial RobustnessFine-Grained Image Classification	CodeCode Available	1
Explored An Effective Methodology for Fine-Grained Snake Recognition	Jul 24, 2022	Fine-Grained Image ClassificationSelf-Supervised Learning	CodeCode Available	0
Few-shot Fine-grained Image Classification via Multi-Frequency Neighborhood and Double-cross Modulation	Jul 18, 2022	ClassificationFine-Grained Image Classification	CodeCode Available	0
ViT-NeT: Interpretable Vision Transformers with Neural Tree Decoder	Jul 17, 2022	Decision MakingDecoder	CodeCode Available	1
Contrastive Deep Supervision	Jul 12, 2022	Contrastive LearningFine-Grained Image Classification	CodeCode Available	1
Adaptive Fine-Grained Predicates Learning for Scene Graph Generation	Jul 11, 2022	Fine-Grained Image ClassificationGraph Generation	—Unverified	0
Learning Cross-Image Object Semantic Relation in Transformer for Few-Shot Fine-Grained Image Classification	Jul 2, 2022	Fine-Grained Image Classificationimage-classification	CodeCode Available	1
0/1 Deep Neural Networks via Block Coordinate Descent	Jun 19, 2022	10-shot image generation	—Unverified	0
Multi-View Active Fine-Grained Recognition	Jun 2, 2022	Fine-Grained Image Classification	CodeCode Available	0
On the Eigenvalues of Global Covariance Pooling for Fine-grained Visual Recognition	May 26, 2022	Fine-Grained Image ClassificationFine-Grained Visual Categorization	—Unverified	0
An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems	May 25, 2022	Continual LearningFine-Grained Image Classification	CodeCode Available	0
Fine-Grained Visual Classification using Self Assessment Classifier	May 21, 2022	ClassificationContinual Learning	CodeCode Available	1
Large Neural Networks Learning from Scratch with Very Few Data and without Explicit Regularization	May 18, 2022	Fine-Grained Image ClassificationImage Augmentation	—Unverified	0
Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification	May 4, 2022	Fine-Grained Image ClassificationFine-Grained Visual Categorization	—Unverified	0

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All datasets Stanford Cars FGVC-Aircraft CUB-200-2011 NABirds Oxford 102 Flowers Stanford Dogs Oxford-IIIT Pets Caltech-101 Food-101 Oxford-IIIT Pet Dataset CompCars Bird-225

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	TResnet-L + PMD	Accuracy	97.3	—	Unverified
2	CMAL-Net	Accuracy	97.1	—	Unverified
3	I2-HOFI	Accuracy	96.92	—	Unverified
4	TResNet-L + ML-Decoder	Accuracy	96.41	—	Unverified
5	DAT	Accuracy	96.2	—	Unverified
6	ALIGN	Accuracy	96.13	—	Unverified
7	SR-GNN	Accuracy	96.1	—	Unverified
8	EffNet-L2 (SAM)	Accuracy	95.96	—	Unverified
9	SaSPA + CAL	Accuracy	95.72	—	Unverified
10	CAP	Accuracy	95.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	I2-HOFI	Accuracy	96.42	—	Unverified
2	SR-GNN	Accuracy	95.4	—	Unverified
3	Inceptionv4	Accuracy	95.11	—	Unverified
4	CAP	Accuracy	94.9	—	Unverified
5	CSQA-Net	Accuracy	94.7	—	Unverified
6	CMAL-Net	Accuracy	94.7	—	Unverified
7	TBMSL-Net	Accuracy	94.7	—	Unverified
8	PART	Accuracy	94.6	—	Unverified
9	SaSPA + CAL	Accuracy	94.5	—	Unverified
10	AENet	Accuracy	94.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	HERBS	Accuracy	93.1	—	Unverified
2	PIM	Accuracy	92.8	—	Unverified
3	MDCM	Accuracy	92.5	—	Unverified
4	IELT	Accuracy	91.8	—	Unverified
5	CAP	Accuracy	91.8	—	Unverified
6	SFETrans	Accuracy	91.8	—	Unverified
7	TransFG	Accuracy	91.7	—	Unverified
8	ViT-NeT	Accuracy	91.7	—	Unverified
9	SWAG (ViT H/14)	Accuracy	91.7	—	Unverified
10	FFVT	Accuracy	91.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MetaFormer (MetaFormer-2,384)	Accuracy	93	—	Unverified
2	HERBS	Accuracy	93	—	Unverified
3	PIM	Accuracy	92.8	—	Unverified
4	ViT-NeT (SwinV2-B)	Accuracy	92.5	—	Unverified
5	MPSA	Accuracy	92.5	—	Unverified
6	CSQA-Net	Accuracy	92.3	—	Unverified
7	I2-HOFI	Accuracy	92.12	—	Unverified
8	MDCM	Accuracy	92	—	Unverified
9	CGL	Accuracy	91.7	—	Unverified
10	SR-GNN	Accuracy	91.2	—	Unverified