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
1LDAM-DRW + SSPError Rate52.89Unverified
2LDAM-DRW-RSGError Rate51.5Unverified
3Hybrid-PSCError Rate51.07Unverified
4CBD+TailCalibXError Rate49.1Unverified
5MetaSAug-LDAMError Rate47.73Unverified
6MiSLASError Rate47.7Unverified
7GCLError Rate46.4Unverified
8TADEError Rate46.1Unverified
9BCL(ResNet-32)Error Rate43.4Unverified
10NCL(ResNet32)Error Rate43.2Unverified