Papers › Long-tailed Recognition by Routing Diverse Distribution-Aware Experts
Long-tailed Recognition by Routing Diverse Distribution-Aware Experts
Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu, Stella X. Yu
Natural data are often long-tail distributed over semantic classes. Existing recognition methods tackle this imbalanced classification by placing more emphasis on the tail data, through class re-balancing/re-weighting or ensembling over different data groups, resulting in increased tail accuracies but reduced head accuracies. We take a dynamic view of the training data and provide a principled model bias and variance analysis as the training data fluctuates: Existing long-tail classifiers invariably increase the model variance and the head-tail model bias gap remains large, due to more and larger confusion with hard negatives for the tail. We propose a new long-tailed classifier called RoutIng Diverse Experts (RIDE). It reduces the model variance with multiple experts, reduces the model bias with a distribution-aware diversity loss, reduces the computational cost with a dynamic expert routing module. RIDE outperforms the state-of-the-art by 5% to 7% on CIFAR100-LT, ImageNet-LT and iNaturalist 2018 benchmarks. It is also a universal framework that is applicable to various backbone networks, long-tailed algorithms, and training mechanisms for consistent performance gains. Our code is available at: https://github.com/frank-xwang/RIDE-LongTailRecognition.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | iNaturalist 2018 | RIDE (ResNet-50) | Top-1 Accuracy | 72.2% | #35 of 60 | Archive leaderboard | report |
| Long-tail Learning | CIFAR-100-LT (ρ=100) | RIDE+distill | Error Rate | 50.9 | #32 of 66 | Archive leaderboard | report |
| Long-tail Learning | CIFAR-100-LT (ρ=100) | RIDE | Error Rate | 52 | #36 of 66 | Archive leaderboard | report |
| Long-tail Learning | ImageNet-LT | RIDE (ResNeXt-50) | Top-1 Accuracy | 56.4 | #33 of 69 | Archive leaderboard | report |
| Long-tail Learning | ImageNet-LT | RIDE (ResNet-50) | Top-1 Accuracy | 54.9 | #39 of 69 | Archive leaderboard | report |
| Long-tail Learning | iNaturalist 2018 | RIDE | Top-1 Accuracy | 72.2% | #26 of 43 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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