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 1–10 of 131 papers

TitleStatusHype
Mitigating Spurious Correlations with Causal Logit Perturbation—0
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 Learning—0
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 Learning—0
Adaptive Parametric ActivationCode2
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Benchmark Results

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