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
All datasetsImageNet-LTCIFAR-100-LT (ρ=100)CIFAR-10-LT (ρ=10)iNaturalist 2018CIFAR-100-LT (ρ=10)Places-LTCIFAR-10-LT (ρ=100)CIFAR-100-LT (ρ=50)MIMIC-CXR-LTNIH-CXR-LTCOCO-MLTVOC-MLT
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | LIFT (ViT-L/14) | Top-1 Accuracy | 53.7 | — | Unverified |
| 2 | LIFT (ViT-B/16) | Top-1 Accuracy | 52.2 | — | Unverified |
| 3 | MAM (ViT-B/16) | Top-1 Accuracy | 51.4 | — | Unverified |
| 4 | VL-LTR (ViT-B-16) | Top-1 Accuracy | 50.1 | — | Unverified |
| 5 | BALLAD(ViT-B-16) | Top-1 Accuracy | 49.5 | — | Unverified |
| 6 | BALLAD(ResNet-50×16) | Top-1 Accuracy | 49.3 | — | Unverified |
| 7 | VL-LTR (ResNet-50) | Top-1 Accuracy | 48 | — | Unverified |
| 8 | BALLAD(ResNet-101) | Top-1 Accuracy | 47.9 | — | Unverified |
| 9 | RAC (ViT-B-16) | Top-1 Accuracy | 47.17 | — | Unverified |
| 10 | BALLAD(ResNet-50) | Top-1 Accuracy | 46.5 | — | Unverified |