Papers › The Majority Can Help The Minority: Context-rich Minority Oversampling for Long-tailed...
The Majority Can Help The Minority: Context-rich Minority Oversampling for Long-tailed Classification
Seulki Park, Youngkyu Hong, Byeongho Heo, Sangdoo Yun, Jin Young Choi
The problem of class imbalanced data is that the generalization performance of the classifier deteriorates due to the lack of data from minority classes. In this paper, we propose a novel minority over-sampling method to augment diversified minority samples by leveraging the rich context of the majority classes as background images. To diversify the minority samples, our key idea is to paste an image from a minority class onto rich-context images from a majority class, using them as background images. Our method is simple and can be easily combined with the existing long-tailed recognition methods. We empirically prove the effectiveness of the proposed oversampling method through extensive experiments and ablation studies. Without any architectural changes or complex algorithms, our method achieves state-of-the-art performance on various long-tailed classification benchmarks. Our code is made available at https://github.com/naver-ai/cmo.
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Code
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Code Syntology ran Syntology
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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 | BS-CMO (ResNet-50) | Top-1 Accuracy | 74.0% | #30 of 60 | Archive leaderboard | report |
| Long-tail Learning | CIFAR-100-LT (ρ=100) | RIDE 3 experts + CMO | Error Rate | 50 | #28 of 66 | Archive leaderboard | report |
| Long-tail Learning | CIFAR-100-LT (ρ=100) | LDAM-DRW + CMO | Error Rate | 52.8 | #38 of 66 | Archive leaderboard | report |
| Long-tail Learning | CIFAR-100-LT (ρ=100) | Balanced Softmax + CMO | Error Rate | 53.4 | #40 of 66 | Archive leaderboard | report |
| Long-tail Learning | CIFAR-100-LT (ρ=100) | CE-DRW | Error Rate | 58.9 | #62 of 66 | Archive leaderboard | report |
| Long-tail Learning | ImageNet-LT | BS-CMO (ResNet-50) | Top-1 Accuracy | 58.0 | #23 of 69 | 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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