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 301–325 of 353 papers

Title	Date	Tasks	Status	Hype
Correcting the Triplet Selection Bias for Triplet Loss	Sep 1, 2018	Face RecognitionFine-Grained Image Classification	CodeCode Available	0
Grassmann Pooling as Compact Homogeneous Bilinear Pooling for Fine-Grained Visual Classification	Sep 1, 2018	Fine-Grained Image ClassificationFine-Grained Visual Recognition	—Unverified	0
Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding	Aug 14, 2018	Fine-Grained Image ClassificationFine-Grained Image Recognition	CodeCode Available	0
Weakly Supervised Bilinear Attention Network for Fine-Grained Visual Classification	Aug 6, 2018	ClassificationFine-Grained Image Classification	—Unverified	0
Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition	Jul 2, 2018	Fine-Grained Image ClassificationFine-Grained Image Recognition	—Unverified	0
Generating Counterfactual Explanations with Natural Language	Jun 26, 2018	AI AgentClassification	—Unverified	0
Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning	Jun 16, 2018	Fine-Grained Image ClassificationFine-Grained Visual Categorization	CodeCode Available	1
Webly Supervised Learning Meets Zero-Shot Learning: A Hybrid Approach for Fine-Grained Classification	Jun 1, 2018	Fine-Grained Image ClassificationGeneral Classification	—Unverified	0
AutoAugment: Learning Augmentation Policies from Data	May 24, 2018	Data AugmentationDomain Generalization	CodeCode Available	3
Do Better ImageNet Models Transfer Better?	May 23, 2018	Fine-Grained Image ClassificationGeneral Classification	—Unverified	0
Unsupervised Learning using Pretrained CNN and Associative Memory Bank	May 2, 2018	Few-Shot Image ClassificationFine-Grained Image Classification	—Unverified	0
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	0
Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization	Dec 4, 2017	Fine-Grained Image ClassificationFine-Grained Image Recognition	CodeCode Available	0
Learning Multi-Attention Convolutional Neural Network for Fine-Grained Image Recognition	Oct 1, 2017	ClusteringFine-Grained Image Classification	CodeCode Available	0
Fast Fine-grained Image Classification via Weakly Supervised Discriminative Localization	Sep 30, 2017	ClassificationFine-Grained Image Classification	—Unverified	0
Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN	Sep 25, 2017	ClassificationFine-Grained Image Classification	—Unverified	0
Where to Focus: Deep Attention-based Spatially Recurrent Bilinear Networks for Fine-Grained Visual Recognition	Sep 18, 2017	Deep AttentionFine-Grained Image Classification	—Unverified	0
Zero-Shot Fine-Grained Classification by Deep Feature Learning with Semantics	Jul 4, 2017	ClassificationDomain Adaptation	—Unverified	0
Fine-Grained Recognition as HSnet Search for Informative Image Parts	Jul 1, 2017	Fine-Grained Image Classificationimage-classification	—Unverified	0
Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition	Jul 1, 2017	Fine-Grained Image ClassificationFine-Grained Image Recognition	—Unverified	0
Fine-Grained Image Classification via Combining Vision and Language	Jul 1, 2017	AttributeClassification	—Unverified	0
Pairwise Confusion for Fine-Grained Visual Classification	May 22, 2017	ClassificationFine-Grained Image Classification	CodeCode Available	0
Integrating Scene Text and Visual Appearance for Fine-Grained Image Classification	Apr 15, 2017	ClassificationFine-Grained Image Classification	—Unverified	0
Fine-graind Image Classification via Combining Vision and Language	Apr 10, 2017	AttributeClassification	—Unverified	0
Object-Part Attention Model for Fine-grained Image Classification	Apr 6, 2017	ClassificationFine-Grained Image Classification	CodeCode Available	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