Long Tail Learning On Imagenet Lt

Métriques

Top-1 Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Top-1 Accuracy	Paper Title	Repository
BatchFormer(ResNet-50, RIDE)	55.7	BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning
Online Feature Augmentation	35.3	Feature Space Augmentation for Long-Tailed Data	-
BALMS	41.8	Balanced Meta-Softmax for Long-Tailed Visual Recognition
VL-LTR (ViT-B-16)	77.2	VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition
APA (SE-ResNet-50)	57.9	Adaptive Parametric Activation	-
DeiT-LT	59.1	DeiT-LT Distillation Strikes Back for Vision Transformer Training on Long-Tailed Datasets
ProCo (ResNeXt50)	58.0	Probabilistic Contrastive Learning for Long-Tailed Visual Recognition
ProCo (ResNet50)	60.2	Probabilistic Contrastive Learning for Long-Tailed Visual Recognition
cRT + SSP	51.3	Rethinking the Value of Labels for Improving Class-Imbalanced Learning
KCL	51.5	Exploring Balanced Feature Spaces for Representation Learning	-
BALLAD(ResNet-101)	70.5	A Simple Long-Tailed Recognition Baseline via Vision-Language Model
BatchFormer(ResNet-50, PaCo)	57.4	BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning
NCL(ResNet-50)	57.4	Nested Collaborative Learning for Long-Tailed Visual Recognition	-
OPeN (ResNeXt-50)	55.1	Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images
TSC(ResNet-50)	52.4	Targeted Supervised Contrastive Learning for Long-Tailed Recognition
smDRAGON	42.0	From Generalized zero-shot learning to long-tail with class descriptors
RIDE (ResNeXt-50)	56.4	Long-tailed Recognition by Routing Diverse Distribution-Aware Experts
DRO-LT	53.5	Distributional Robustness Loss for Long-tail Learning	-
LDAM + DRW + SAM	53.1	Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data
LIFT (ViT-B/16)	78.3	Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts

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