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 26–50 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 | 82.9 | — | Unverified |
| 2 | µ2Net+ (ViT-L/16) | Top-1 Accuracy | 82.5 | — | Unverified |
| 3 | MAM (ViT-B/16) | Top-1 Accuracy | 82.3 | — | Unverified |
| 4 | LIFT (ViT-B/16) | Top-1 Accuracy | 78.3 | — | Unverified |
| 5 | VL-LTR (ViT-B-16) | Top-1 Accuracy | 77.2 | — | Unverified |
| 6 | BALLAD(ResNet-50×16) | Top-1 Accuracy | 76.5 | — | Unverified |
| 7 | BALLAD(ViT-B-16) | Top-1 Accuracy | 75.7 | — | Unverified |
| 8 | BALLAD(ResNet-101) | Top-1 Accuracy | 70.5 | — | Unverified |
| 9 | VL-LTR (ResNet-50) | Top-1 Accuracy | 70.1 | — | Unverified |
| 10 | BALLAD(ResNet-50) | Top-1 Accuracy | 67.2 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Cross-Entropy (CE) | Error Rate | 62.75 | — | Unverified |
| 2 | Cross-Entropy (CE) | Error Rate | 61.68 | — | Unverified |
| 3 | IBLLoss | Error Rate | 61.52 | — | Unverified |
| 4 | Cross-Entropy + Curvature Regularization | Error Rate | 59.5 | — | Unverified |
| 5 | CE-DRW | Error Rate | 58.9 | — | Unverified |
| 6 | LDAM-DRW | Error Rate | 57.96 | — | Unverified |
| 7 | ELP | Error Rate | 57.6 | — | Unverified |
| 8 | CDB-loss | Error Rate | 57.43 | — | Unverified |
| 9 | CE-DRW-IC | Error Rate | 56.9 | — | Unverified |
| 10 | LDAM-DRW + SSP | Error Rate | 56.57 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | RISDA | Error Rate | 20.11 | — | Unverified |
| 2 | Empirical Risk Minimization (ERM, CE) | Error Rate | 13.61 | — | Unverified |
| 3 | Class-balanced Reweighting | Error Rate | 13.46 | — | Unverified |
| 4 | Class-balanced Resampling | Error Rate | 13.21 | — | Unverified |
| 5 | IBLLoss | Error Rate | 12.93 | — | Unverified |
| 6 | Class-balanced Focal Loss | Error Rate | 12.9 | — | Unverified |
| 7 | DecTDE | Error Rate | 12.63 | — | Unverified |
| 8 | M2m | Error Rate | 12.5 | — | Unverified |
| 9 | Prior-LT | Error Rate | 12.2 | — | Unverified |
| 10 | ELF&LDAM+DRW | Error Rate | 12 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | LIFT (ViT-L/14@336px) | Top-1 Accuracy | 87.4 | — | Unverified |
| 2 | LIFT (ViT-L/14) | Top-1 Accuracy | 85.2 | — | Unverified |
| 3 | GML (ViT-B-16) | Top-1 Accuracy | 82.1 | — | Unverified |
| 4 | LIFT (ViT-B/16) | Top-1 Accuracy | 80.4 | — | Unverified |
| 5 | RAC (ViT-B-16) | Top-1 Accuracy | 80.24 | — | Unverified |
| 6 | GPaCo (2-R152) | Top-1 Accuracy | 79.8 | — | Unverified |
| 7 | TADE(ResNet-152) | Top-1 Accuracy | 77 | — | Unverified |
| 8 | ProCo (ResNet50) | Top-1 Accuracy | 75.8 | — | Unverified |
| 9 | MDCS(Resnet50) | Top-1 Accuracy | 75.6 | — | Unverified |
| 10 | DeiT-LT | Top-1 Accuracy | 75.1 | — | Unverified |