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
1ETF Classifier + DR (Resnet)Error Rate23.5Unverified
2LDAM-DRWError Rate22.97Unverified
3LDAM-DRW + SSPError Rate22.17Unverified
4ELPError Rate22Unverified
5TSC(ResNet-32)Error Rate21.3Unverified
6CE+DRS+GITError Rate21.24Unverified
7smDRAGONError Rate20.37Unverified
8TLC (4 experts)Error Rate19.6Unverified
9MetaSAug-LDAMError Rate19.34Unverified
10ACE (4 experts)Error Rate18.6Unverified