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Long Tail Learning On Inaturalist 2018

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Top-1 Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Paper Title
LIFT (ViT-L/14@336px)87.4%Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
LIFT (ViT-L/14)85.2%Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
GML (ViT-B-16)82.1%Long-Tailed Recognition by Mutual Information Maximization between Latent Features and Ground-Truth Labels
VL-LTR (ViT-B-16)81.0%VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition
LIFT (ViT-B/16)80.4%Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
RAC (ViT-B-16)80.24%Retrieval Augmented Classification for Long-Tail Visual Recognition
GPaCo (2-R152)79.8%Generalized Parametric Contrastive Learning
GPaCo (ResNet-152)78.1%Generalized Parametric Contrastive Learning
TADE(ResNet-152)77%Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition
ProCo (ResNet50)75.8%Probabilistic Contrastive Learning for Long-Tailed Visual Recognition
MDCS(Resnet50)75.6%MDCS: More Diverse Experts with Consistency Self-distillation for Long-tailed Recognition
GPaCo (ResNet-50)75.4%Generalized Parametric Contrastive Learning
CBD-ENS (ResNet-101)75.3%Class-Balanced Distillation for Long-Tailed Visual Recognition
PaCo(ResNet-152)75.2%Parametric Contrastive Learning
DeiT-LT75.1%DeiT-LT Distillation Strikes Back for Vision Transformer Training on Long-Tailed Datasets
APA (SE-ResNet-50)74.8Adaptive Parametric Activation
VL-LTR (ResNet-50)74.6%VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition
GML (ResNet-50)74.5%Long-Tailed Recognition by Mutual Information Maximization between Latent Features and Ground-Truth Labels
NCL(ResNet-50)74.2%Nested Collaborative Learning for Long-Tailed Visual Recognition
BatchFormer(ResNet-50, RIDE)74.1%BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning
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Long Tail Learning On Inaturalist 2018 | SOTA | HyperAI