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
1Decoupling (cRT)Balanced Accuracy0.3Unverified
2Reweighted LDAM-DRWBalanced Accuracy0.28Unverified
3Class-balanced LDAM-DRWBalanced Accuracy0.27Unverified
4Reweighted LDAMBalanced Accuracy0.24Unverified
5Reweighted Focal LossBalanced Accuracy0.24Unverified
6Decoupling (tau-norm)Balanced Accuracy0.23Unverified
7Class-balanced SoftmaxBalanced Accuracy0.23Unverified
8Class-balanced LDAMBalanced Accuracy0.23Unverified
9Reweighted SoftmaxBalanced Accuracy0.21Unverified
10Class-balanced Focal LossBalanced Accuracy0.19Unverified