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Long-tail Learning

Long-tailed learning, one of the most challenging problems in visual recognition, aims to train well-performing models from a large number of images that follow a long-tailed class distribution.

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 41–50 of 131 papers

Title	Date	Tasks	Status	Hype
Class-Wise Difficulty-Balanced Loss for Solving Class-Imbalance	Oct 5, 2020	Long-tail Learning	CodeCode Available	1
Equalization Loss for Long-Tailed Object Recognition	Mar 11, 2020	Long-tail LearningObject	CodeCode Available	1
Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data	Dec 28, 2022	Long-tail Learning	CodeCode Available	1
Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition	Oct 8, 2024	Contrastive LearningDensity Estimation	CodeCode Available	1
Influence-Balanced Loss for Imbalanced Visual Classification	Oct 6, 2021	ClassificationLong-tail Learning	CodeCode Available	1
Feature Generation for Long-tail Classification	Nov 10, 2021	ClassificationFew-Shot Learning	CodeCode Available	1
Disentangling Label Distribution for Long-tailed Visual Recognition	Dec 1, 2020	Image ClassificationLong-tail Learning	CodeCode Available	1
FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated Distillation	Apr 30, 2022	Federated LearningLong-tail Learning	CodeCode Available	1
Global and Local Mixture Consistency Cumulative Learning for Long-tailed Visual Recognitions	May 15, 2023	Long-tail Learning	CodeCode Available	1
Invariant Feature Learning for Generalized Long-Tailed Classification	Jul 19, 2022	AttributeClassification	CodeCode Available	1

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All datasets ImageNet-LT CIFAR-100-LT (ρ=100)CIFAR-10-LT (ρ=10)iNaturalist 2018 CIFAR-100-LT (ρ=10)Places-LT CIFAR-10-LT (ρ=100)CIFAR-100-LT (ρ=50)MIMIC-CXR-LT NIH-CXR-LT COCO-MLT VOC-MLT

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	LIFT (ViT-L/14)	Top-1 Accuracy	82.9	—	Unverified
2	µ2Net+ (ViT-L/16)	Top-1 Accuracy	82.5	—	Unverified
3	MAM (ViT-B/16)	Top-1 Accuracy	82.3	—	Unverified
4	LIFT (ViT-B/16)	Top-1 Accuracy	78.3	—	Unverified
5	VL-LTR (ViT-B-16)	Top-1 Accuracy	77.2	—	Unverified
6	BALLAD(ResNet-50×16)	Top-1 Accuracy	76.5	—	Unverified
7	BALLAD(ViT-B-16)	Top-1 Accuracy	75.7	—	Unverified
8	BALLAD(ResNet-101)	Top-1 Accuracy	70.5	—	Unverified
9	VL-LTR (ResNet-50)	Top-1 Accuracy	70.1	—	Unverified
10	BALLAD(ResNet-50)	Top-1 Accuracy	67.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Cross-Entropy (CE)	Error Rate	62.75	—	Unverified
2	Cross-Entropy (CE)	Error Rate	61.68	—	Unverified
3	IBLLoss	Error Rate	61.52	—	Unverified
4	Cross-Entropy + Curvature Regularization	Error Rate	59.5	—	Unverified
5	CE-DRW	Error Rate	58.9	—	Unverified
6	LDAM-DRW	Error Rate	57.96	—	Unverified
7	ELP	Error Rate	57.6	—	Unverified
8	CDB-loss	Error Rate	57.43	—	Unverified
9	CE-DRW-IC	Error Rate	56.9	—	Unverified
10	LDAM-DRW + SSP	Error Rate	56.57	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RISDA	Error Rate	20.11	—	Unverified
2	Empirical Risk Minimization (ERM, CE)	Error Rate	13.61	—	Unverified
3	Class-balanced Reweighting	Error Rate	13.46	—	Unverified
4	Class-balanced Resampling	Error Rate	13.21	—	Unverified
5	IBLLoss	Error Rate	12.93	—	Unverified
6	Class-balanced Focal Loss	Error Rate	12.9	—	Unverified
7	DecTDE	Error Rate	12.63	—	Unverified
8	M2m	Error Rate	12.5	—	Unverified
9	Prior-LT	Error Rate	12.2	—	Unverified
10	KCL	Error Rate	12	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LIFT (ViT-L/14@336px)	Top-1 Accuracy	87.4	—	Unverified
2	LIFT (ViT-L/14)	Top-1 Accuracy	85.2	—	Unverified
3	GML (ViT-B-16)	Top-1 Accuracy	82.1	—	Unverified
4	LIFT (ViT-B/16)	Top-1 Accuracy	80.4	—	Unverified
5	RAC (ViT-B-16)	Top-1 Accuracy	80.24	—	Unverified
6	GPaCo (2-R152)	Top-1 Accuracy	79.8	—	Unverified
7	TADE(ResNet-152)	Top-1 Accuracy	77	—	Unverified
8	ProCo (ResNet50)	Top-1 Accuracy	75.8	—	Unverified
9	MDCS(Resnet50)	Top-1 Accuracy	75.6	—	Unverified
10	DeiT-LT	Top-1 Accuracy	75.1	—	Unverified