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SotA/Methodology/Long-tail Learning

Long-tail Learning

29 benchmarks131 papers

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.

Benchmarks

Long-tail Learning on ImageNet-LT

Top-1 Accuracy

Long-tail Learning on CIFAR-100-LT (ρ=100)

Error Rate

Long-tail Learning on CIFAR-10-LT (ρ=10)

Error Rate

Long-tail Learning on CIFAR-100-LT (ρ=10)

Error Rate

Long-tail Learning on Places-LT

Top-1 AccuracyTop 1 Accuracy

Long-tail Learning on CIFAR-10-LT (ρ=100)

Error Rate

Long-tail Learning on CIFAR-100-LT (ρ=50)

Error Rate

Long-tail Learning on MIMIC-CXR-LT

Balanced Accuracy

Long-tail Learning on NIH-CXR-LT

Balanced Accuracy

Long-tail Learning on COCO-MLT

Average mAP

Long-tail Learning on VOC-MLT

Average mAP

Long-tail Learning on CIFAR-10-LT (ρ=50)

Error Rate

Long-tail Learning on ImageNet-GLT

Accuracy

Long-tail Learning on AWA-LT

Per-Class AccuracyLong-Tailed Accuracy

Long-tail Learning on CUB-LT

Per-Class AccuracyLong-Tailed Accuracy

Long-tail Learning on ImageNet-LT-d

Per-Class Accuracy

Long-tail Learning on SUN-LT

Per-Class AccuracyLong-Tailed Accuracy

Long-tail Learning on EGTEA

Average PrecisionAverage Recall

Long-tail Learning on iNaturalist 2018

Top-1 Accuracy

Long-tail Learning on CIFAR-10-LT (ρ=200)

Error Rate

Long-tail Learning on CIFAR-100-LT (ρ=200)

Error Rate

Long-tail Learning on CelebA-5

Error Rate

Long-tail Learning on Lot-insts

Macro-F1

Long-tail Learning on mini-ImageNet-LT

Error Rate