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
1LMPT(ViT-B/16)Average mAP87.88Unverified
2LMPT(ResNet-50)Average mAP85.44Unverified
3LTML(ResNet-50)Average mAP81.44Unverified
4PG Loss(ResNet-50)Average mAP80.37Unverified
5DB Focal(ResNet-50)Average mAP78.94Unverified
6RS(ResNet-50)Average mAP75.38Unverified
7CB Focal(ResNet-50)Average mAP75.24Unverified
8Focal Loss(ResNet-50)Average mAP73.88Unverified
9OLTR(ResNet-50)Average mAP71.02Unverified
10LDAM(ResNet-50)Average mAP70.73Unverified