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 326–350 of 353 papers

Title	Date	Tasks	Status	Hype
Deep CNNs With Spatially Weighted Pooling for Fine-Grained Car Recognition	Apr 4, 2017	Fine-Grained Image Classification	CodeCode Available	0
Convolutional Low-Resolution Fine-Grained Classification	Mar 15, 2017	ClassificationFine-Grained Image Classification	—Unverified	0
Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro	Jan 26, 2017	Fine-Grained Image ClassificationGenerative Adversarial Network	CodeCode Available	1
Deep Quantization: Encoding Convolutional Activations with Deep Generative Model	Nov 29, 2016	Action RecognitionFine-Grained Image Classification	—Unverified	0
Gaze Embeddings for Zero-Shot Image Classification	Nov 28, 2016	ClassificationFine-Grained Image Classification	—Unverified	0
Generalized BackPropagation, Étude De Cas: Orthogonality	Nov 17, 2016	Dimensionality ReductionFine-Grained Image Classification	—Unverified	0
Detecting Visually Relevant Sentences for Fine-Grained Classification	Aug 1, 2016	ClassificationFine-Grained Image Classification	—Unverified	0
Part-Stacked CNN for Fine-Grained Visual Categorization	Dec 26, 2015	ClassificationFine-Grained Image Classification	—Unverified	0
Embedding Label Structures for Fine-Grained Feature Representation	Dec 9, 2015	Fine-Grained Image ClassificationGeneral Classification	—Unverified	0
Fine-grained Image Classification by Exploring Bipartite-Graph Labels	Dec 8, 2015	ClassificationFine-Grained Image Classification	—Unverified	0
Bilinear CNN Models for Fine-Grained Visual Recognition	Dec 1, 2015	Fine-Grained Image ClassificationFine-Grained Visual Recognition	—Unverified	0
Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks	Nov 30, 2015	ClassificationFine-Grained Image Classification	—Unverified	0
The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition	Nov 20, 2015	Active LearningFine-Grained Image Classification	CodeCode Available	0
Fine-grained Recognition Datasets for Biodiversity Analysis	Jul 3, 2015	ClassificationFine-Grained Image Classification	—Unverified	0
A Large-Scale Car Dataset for Fine-Grained Categorization and Verification	Jun 30, 2015	Fine-Grained Image ClassificationGeneral Classification	CodeCode Available	0
Hyper-Class Augmented and Regularized Deep Learning for Fine-Grained Image Classification	Jun 1, 2015	Fine-Grained Image ClassificationGeneral Classification	—Unverified	0
Bilinear CNNs for Fine-grained Visual Recognition	Apr 29, 2015	Fine-Grained Image ClassificationFine-Grained Visual Recognition	CodeCode Available	0
Weakly Supervised Fine-Grained Image Categorization	Apr 20, 2015	Fine-Grained Image ClassificationImage Categorization	—Unverified	0
Modelling Local Deep Convolutional Neural Network Features to Improve Fine-Grained Image Classification	Feb 27, 2015	Fine-Grained Image ClassificationGeneral Classification	—Unverified	0
Object-centric Sampling for Fine-grained Image Classification	Dec 10, 2014	ClassificationFine-Grained Image Classification	—Unverified	0
The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classification	Nov 24, 2014	ClassificationFine-Grained Image Classification	—Unverified	0
Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors	Nov 24, 2014	Fine-Grained Image ClassificationGeneral Classification	—Unverified	0
Evaluation of Output Embeddings for Fine-Grained Image Classification	Sep 30, 2014	ClassificationFew-Shot Image Classification	CodeCode Available	0
10,000+ Times Accelerated Robust Subset Selection (ARSS)	Sep 12, 2014	10-shot image generationAction Recognition	—Unverified	0
Part-based R-CNNs for Fine-grained Category Detection	Jul 15, 2014	Fine-Grained Image ClassificationObject	—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	SWAG (ViT H/14)	Accuracy	91.7	—	Unverified
8	ViT-NeT	Accuracy	91.7	—	Unverified
9	TransFG	Accuracy	91.7	—	Unverified
10	I2-HOFI	Accuracy	91.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	HERBS	Accuracy	93	—	Unverified
2	MetaFormer (MetaFormer-2,384)	Accuracy	93	—	Unverified
3	PIM	Accuracy	92.8	—	Unverified
4	MPSA	Accuracy	92.5	—	Unverified
5	ViT-NeT (SwinV2-B)	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