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
1CE-DRW-ICError Rate41.4Unverified
2LDAM-DRWError Rate41.29Unverified
3CDB-lossError Rate41.26Unverified
4smDRAGONError Rate41.23Unverified
5LDAM-DRW + SSPError Rate41.09Unverified
6ELPError Rate40.9Unverified
7CBD+TailCalibXError Rate38.87Unverified
8UniMix+Bayias (ResNet-32)Error Rate38.75Unverified
9MetaSAug-LDAMError Rate38.72Unverified
10RIDE + CMO + Curvature RegularizationError Rate38.6Unverified