Papers › Generalized Parametric Contrastive Learning
Generalized Parametric Contrastive Learning
Jiequan Cui, Zhisheng Zhong, Zhuotao Tian, Shu Liu, Bei Yu, Jiaya Jia
In this paper, we propose the Generalized Parametric Contrastive Learning (GPaCo/PaCo) which works well on both imbalanced and balanced data. Based on theoretical analysis, we observe that supervised contrastive loss tends to bias high-frequency classes and thus increases the difficulty of imbalanced learning. We introduce a set of parametric class-wise learnable centers to rebalance from an optimization perspective. Further, we analyze our GPaCo/PaCo loss under a balanced setting. Our analysis demonstrates that GPaCo/PaCo can adaptively enhance the intensity of pushing samples of the same class close as more samples are pulled together with their corresponding centers and benefit hard example learning. Experiments on long-tailed benchmarks manifest the new state-of-the-art for long-tailed recognition. On full ImageNet, models from CNNs to vision transformers trained with GPaCo loss show better generalization performance and stronger robustness compared with MAE models. Moreover, GPaCo can be applied to the semantic segmentation task and obvious improvements are observed on the 4 most popular benchmarks. Our code is available at https://github.com/dvlab-research/Parametric-Contrastive-Learning.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Domain Generalization | ImageNet-C | GPaCo (ViT-L) | mean Corruption Error (mCE) | 39.0 | #13 of 47 | Archive leaderboard | report |
| Domain Generalization | ImageNet-R | GPaCo (ViT-L) | Top-1 Error Rate | 39.7 | #15 of 39 | Archive leaderboard | report |
| Domain Generalization | ImageNet-Sketch | GPaCo (ViT-L) | Top-1 accuracy | 48.3 | #12 of 20 | Archive leaderboard | report |
| Image Classification | ImageNet | GPaCo (ViT-L) | Top 1 Accuracy | 86.01% | #176 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | GPaCo (Vit-B) | Top 1 Accuracy | 84.0% | #361 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | GPaCo (ResNet-50) | Top 1 Accuracy | 79.7% | #745 of 1060 | Archive leaderboard | report |
| Image Classification | iNaturalist 2018 | GPaCo (ResNet-152) | Top-1 Accuracy | 78.1% | #21 of 60 | Archive leaderboard | report |
| Image Classification | iNaturalist 2018 | GPaCo (ResNet-50) | Top-1 Accuracy | 75.4% | #25 of 60 | Archive leaderboard | report |
| Long-tail Learning | ImageNet-LT | GPaCo (2-ResNeXt101-32x4d) | Top-1 Accuracy | 63.2 | #11 of 69 | Archive leaderboard | report |
| Long-tail Learning | Places-LT | GPaCo (ResNet-152) | Top-1 Accuracy | 41.7 | #13 of 29 | Archive leaderboard | report |
| Long-tail Learning | iNaturalist 2018 | GPaCo (2-R152) | Top-1 Accuracy | 79.8% | #7 of 43 | Archive leaderboard | report |
| Long-tail Learning | iNaturalist 2018 | GPaCo (ResNet-152) | Top-1 Accuracy | 78.1% | #8 of 43 | Archive leaderboard | report |
| Long-tail Learning | iNaturalist 2018 | GPaCo (ResNet-50) | Top-1 Accuracy | 75.4% | #12 of 43 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | GPaCo (Swin-L) | Validation mIoU | 54.3 | #64 of 235 | Archive leaderboard | report |
| Semantic Segmentation | PASCAL Context | GPaCo (ResNet101) | mIoU | 56.2 | #25 of 66 | 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.
Methods
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