SOTAVerified

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

Showing 110 of 131 papers

TitleStatusHype
Mitigating Spurious Correlations with Causal Logit Perturbation0
LIFT+: Lightweight Fine-Tuning for Long-Tail LearningCode0
Improving Visual Prompt Tuning by Gaussian Neighborhood Minimization for Long-Tailed Visual RecognitionCode1
Learning from Neighbors: Category Extrapolation for Long-Tail Learning0
Continuous Contrastive Learning for Long-Tailed Semi-Supervised RecognitionCode1
AUCSeg: AUC-oriented Pixel-level Long-tail Semantic SegmentationCode1
Representation Norm Amplification for Out-of-Distribution Detection in Long-Tail LearningCode0
LTRL: Boosting Long-tail Recognition via Reflective LearningCode1
On Characterizing and Mitigating Imbalances in Multi-Instance Partial Label Learning0
Adaptive Parametric ActivationCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LIFT (ViT-L/14@336px)Top-1 Accuracy87.4Unverified
2LIFT (ViT-L/14)Top-1 Accuracy85.2Unverified
3GML (ViT-B-16)Top-1 Accuracy82.1Unverified
4LIFT (ViT-B/16)Top-1 Accuracy80.4Unverified
5RAC (ViT-B-16)Top-1 Accuracy80.24Unverified
6GPaCo (2-R152)Top-1 Accuracy79.8Unverified
7TADE(ResNet-152)Top-1 Accuracy77Unverified
8ProCo (ResNet50)Top-1 Accuracy75.8Unverified
9MDCS(Resnet50)Top-1 Accuracy75.6Unverified
10DeiT-LTTop-1 Accuracy75.1Unverified