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

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Showing 241–250 of 353 papers

Title	Date	Tasks	Status
OmniVec2 - A Novel Transformer based Network for Large Scale Multimodal and Multitask Learning	Jan 1, 2024	3D Point Cloud ClassificationAction Classification	—Unverified
OmniVec: Learning robust representations with cross modal sharing	Nov 7, 2023	3D Point Cloud ClassificationAction Classification	—Unverified
On the Eigenvalues of Global Covariance Pooling for Fine-grained Visual Recognition	May 26, 2022	Fine-Grained Image ClassificationFine-Grained Visual Categorization	—Unverified
On the Ideal Number of Groups for Isometric Gradient Propagation	Feb 7, 2023	Fine-Grained Image ClassificationImage Classification	—Unverified
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision	Feb 16, 2022	Action ClassificationAction Recognition	—Unverified
Aligned to the Object, not to the Image: A Unified Pose-aligned Representation for Fine-grained Recognition	Jan 27, 2018	Fine-Grained Image ClassificationObject	—Unverified
Exploring Target Driven Image Classification	Jan 1, 2021	ClassificationFine-Grained Image Classification	—Unverified
Part-based R-CNNs for Fine-grained Category Detection	Jul 15, 2014	Fine-Grained Image ClassificationObject	—Unverified
Semantically-Prompted Language Models Improve Visual Descriptions	Jun 5, 2023	ClassificationDescriptive	—Unverified
Enhancing Multimodal In-Context Learning for Image Classification through Coreset Optimization	Apr 19, 2025	ClassificationFine-Grained Image Classification	—Unverified

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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	CMAL-Net	Accuracy	94.7	—	Unverified
6	TBMSL-Net	Accuracy	94.7	—	Unverified
7	CSQA-Net	Accuracy	94.7	—	Unverified
8	PART	Accuracy	94.6	—	Unverified
9	AENet	Accuracy	94.5	—	Unverified
10	SaSPA + CAL	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	SFETrans	Accuracy	91.8	—	Unverified
5	CAP	Accuracy	91.8	—	Unverified
6	IELT	Accuracy	91.8	—	Unverified
7	TransFG	Accuracy	91.7	—	Unverified
8	SWAG (ViT H/14)	Accuracy	91.7	—	Unverified
9	ViT-NeT	Accuracy	91.7	—	Unverified
10	FFVT	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	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