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 101–110 of 353 papers

Title	Date	Tasks	Status	Hype
Focus Longer to See Better:Recursively Refined Attention for Fine-Grained Image Classification	May 22, 2020	Fine-Grained Image ClassificationGeneral Classification	CodeCode Available	1
Neural Architecture Transfer	May 12, 2020	Fine-Grained Image Classificationimage-classification	CodeCode Available	1
Gradient Centralization: A New Optimization Technique for Deep Neural Networks	Apr 3, 2020	Fine-Grained Image ClassificationGeneral Classification	CodeCode Available	1
Look-into-Object: Self-supervised Structure Modeling for Object Recognition	Mar 31, 2020	Fine-Grained Image ClassificationImage Recognition	CodeCode Available	1
Proxy Anchor Loss for Deep Metric Learning	Mar 31, 2020	Fine-Grained Image ClassificationFine-Grained Vehicle Classification	CodeCode Available	1
TResNet: High Performance GPU-Dedicated Architecture	Mar 30, 2020	Fine-Grained Image ClassificationGeneral Classification	CodeCode Available	1
Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNets	Mar 30, 2020	Fine-Grained Image ClassificationImage Classification	CodeCode Available	1
Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization	Mar 20, 2020	Fine-Grained Image ClassificationFine-Grained Image Recognition	CodeCode Available	1
Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches	Mar 8, 2020	ClassificationFine-Grained Image Classification	CodeCode Available	1
Learning Attentive Pairwise Interaction for Fine-Grained Classification	Feb 24, 2020	ClassificationFine-Grained Image Classification	CodeCode Available	1

Show:10 25 50

← PrevPage 11 of 36Next →

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