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
1LIFT (ViT-L/14)Top-1 Accuracy53.7Unverified
2LIFT (ViT-B/16)Top-1 Accuracy52.2Unverified
3MAM (ViT-B/16)Top-1 Accuracy51.4Unverified
4VL-LTR (ViT-B-16)Top-1 Accuracy50.1Unverified
5BALLAD(ViT-B-16)Top-1 Accuracy49.5Unverified
6BALLAD(ResNet-50×16)Top-1 Accuracy49.3Unverified
7VL-LTR (ResNet-50)Top-1 Accuracy48Unverified
8BALLAD(ResNet-101)Top-1 Accuracy47.9Unverified
9RAC (ViT-B-16)Top-1 Accuracy47.17Unverified
10BALLAD(ResNet-50)Top-1 Accuracy46.5Unverified