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 276–300 of 353 papers

Title	Date	Tasks	Status	Hype
Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification	Aug 4, 2019	ClassificationFew-Shot Learning	—Unverified	0
Competing Ratio Loss for Discriminative Multi-class Image Classification	Jul 31, 2019	Age EstimationClassification	—Unverified	0
Few-shot Learning for Domain-specific Fine-grained Image Classification	Jul 23, 2019	ClassificationFew-Shot Learning	—Unverified	0
Understanding More about Human and Machine Attention in Deep Neural Networks	Jun 20, 2019	Fine-Grained Image ClassificationImage Classification	—Unverified	0
Fixing the train-test resolution discrepancy	Jun 14, 2019	Data AugmentationFine-Grained Image Classification	CodeCode Available	2
Presence-Only Geographical Priors for Fine-Grained Image Classification	Jun 12, 2019	ClassificationFine-Grained Image Classification	CodeCode Available	1
Pay Attention to Convolution Filters: Towards Fast and Accurate Fine-Grained Transfer Learning	Jun 12, 2019	Fine-Grained Image ClassificationGeneral Classification	—Unverified	0
Geo-Aware Networks for Fine-Grained Recognition	Jun 4, 2019	Fine-Grained Image ClassificationGeneral Classification	CodeCode Available	0
IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition	Jun 1, 2019	ClassificationFine-Grained Image Classification	CodeCode Available	0
Destruction and Construction Learning for Fine-Grained Image Recognition	Jun 1, 2019	ClassificationFine-Grained Image Classification	CodeCode Available	1
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks	May 28, 2019	Action RecognitionDomain Generalization	CodeCode Available	3
Contextual Recurrent Convolutional Model for Robust Visual Learning	May 1, 2019	Fine-Grained Image ClassificationGeneral Classification	—Unverified	0
Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognition	Mar 14, 2019	Fine-Grained Image ClassificationFine-Grained Image Recognition	CodeCode Available	0
Spatial-Aware Non-Local Attention for Fashion Landmark Detection	Mar 11, 2019	Fine-Grained Image Classificationimage-classification	—Unverified	0
Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom Up	Mar 7, 2019	Fine-Grained Image ClassificationGeneral Classification	CodeCode Available	0
Unsupervised Part Mining for Fine-grained Image Classification	Feb 26, 2019	ClassificationFine-Grained Image Classification	—Unverified	0
See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification	Jan 26, 2019	Data AugmentationFine-Grained Image Classification	CodeCode Available	1
Fine-Grained Vehicle Classification with Unsupervised Parts Co-occurrence Learning	Jan 23, 2019	ClassificationFine-Grained Image Classification	—Unverified	0
Attribute-Aware Attention Model for Fine-grained Representation Learning	Jan 2, 2019	AttributeFine-Grained Image Classification	CodeCode Available	0
Learning from Web Data: the Benefit of Unsupervised Object Localization	Dec 21, 2018	Fine-Grained Image ClassificationGeneral Classification	—Unverified	0
Maximum-Entropy Fine Grained Classification	Dec 1, 2018	ClassificationDiversity	—Unverified	0
Domain Adaptive Transfer Learning with Specialist Models	Nov 16, 2018	Domain AdaptationFine-Grained Image Classification	—Unverified	0
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism	Nov 16, 2018	Fine-Grained Image Classificationimage-classification	CodeCode Available	2
Maximum-Entropy Fine-Grained Classification	Sep 16, 2018	ClassificationDiversity	—Unverified	0
Learning to Navigate for Fine-grained Classification	Sep 2, 2018	Fine-Grained Image ClassificationGeneral Classification	CodeCode Available	1

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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