This repository contains the code of our papers on the topic of imbalanced learning for recognition.

• The code for our preprint paper "Generalized Parametric Contrastive Learning" is released;
• The code for our preprint paper "Region Rebalance for Long-Tailed Semantic Segmentation" (paper) will be released soon;
• The code for our TPAMI 2022 paper "Residual Learning for Long-tailed recogntion" (paper and code);
• The code for our ICCV 2021 paper "Parametric Contrastive Learning" (paper and code);

This repository contains the implementation code for ICCV2021 paper Parametric Contrastive Learning (https://arxiv.org/abs/2107.12028) and arXiv paper Generalized Parametric Contrastive Learning (https://arxiv.org/abs/2209.12400).

We use python3.8, pytorch 1.8.1, mmcv 1.3.13 and timm==0.3.2. Our code is based on PaCo, MAE, and mmseg.

We use 8 Nvidia GForce RTx 3090 GPUs. MAE pretrained models should be downloaded from here.

cd GPaCo/LT
bash sh/ImageNet/train_resnet50.sh
bash sh/ImageNet/eval_resnet50.sh

cd GPaCo/MAE-ViTs
bash sh/finetune_base_mae.sh
bash sh/finetune_base_mae_multitask.sh
bash sh/finetune_base_mae_gpaco.sh
bash sh/finetune_base_mae_gpaco_eval.sh

cd GPaCo/LT
bash sh/LT/ImageNetLT_train_X50_multitask.sh
bash sh/LT/ImageNetLT_train_X50.sh
sh/LT/ImageNetLT_eval_X50.sh

bash sh/LT/Inat_train_R50.sh
sh/LT/Inat_eval_R50.sh

bash sh/LT/PlacesLT_train_R152.sh
bash sh/LT/PlacesLT_eval_R152.sh

cd GPaCo/Seg/semseg
bash sh/ablation_paco_ade20k/upernet_swinbase_160k_ade20k_paco.sh
bash sh/ablation_paco_coco10k/r50_deeplabv3plus_40k_coco10k_paco.sh
bash sh/ablation_paco_context/r50_deeplabv3plus_40k_context_paco.sh
bash sh/ablation_paco_cityscapes/r50_deeplabv3plus_40k_context.sh

This repository contains the implementation code for ICCV2021 paper:
Parametric Contrastive Learning (https://arxiv.org/abs/2107.12028)

In this paper, we propose Parametric Contrastive Learning (PaCo) to tackle long-tailed recognition. Based on theoretical analysis, we observe supervised contrastive loss tends to bias on high-frequency classes and thus increases the difficulty of imbalance learning. We introduce a set of parametric class-wise learnable centers to rebalance from an optimization perspective. Further, we analyze our PaCo loss under a balanced setting. Our analysis demonstrates that 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 CIFAR, ImageNet, Places, and iNaturalist 2018 manifest the new state-of-the-art for long-tailed recognition. On full ImageNet, models trained with PaCo loss surpass supervised contrastive learning across various ResNet backbones.

For full ImageNet, ImageNet-LT, iNaturalist 2018, Places-LT training and evaluation. Note that PyTorch>=1.6. All experiments are conducted on 4 GPUs. If you have more GPU resources, please make sure that the learning rate should be linearly scaled and 32 images per gpu is recommented.

cd Full-ImageNet
bash sh/train_resnet50.sh
bash sh/eval_resnet50.sh

cd LT
bash sh/ImageNetLT_train_R50.sh
bash sh/ImageNetLT_eval_R50.sh
bash sh/PlacesLT_train_R152.sh
bash sh/PlacesLT_eval_R152.sh

cd LT
bash sh/CIFAR100_train_imb0.1.sh

If you have any questions, feel free to contact us through email ([email protected]) or Github issues. Enjoy!

If you find this code or idea useful, please consider citing our work:

@article{cui2022generalized,
  title={Generalized Parametric Contrastive Learning},
  author={Cui, Jiequan and Zhong, Zhisheng and Tian, Zhuotao and Liu, Shu and Yu, Bei and Jia, Jiaya},
  journal={arXiv preprint arXiv:2209.12400},
  year={2022}
}

@inproceedings{cui2021parametric,
  title={Parametric contrastive learning},
  author={Cui, Jiequan and Zhong, Zhisheng and Liu, Shu and Yu, Bei and Jia, Jiaya},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={715--724},
  year={2021}
}

@ARTICLE{9774921,
  author={Cui, Jiequan and Liu, Shu and Tian, Zhuotao and Zhong, Zhisheng and Jia, Jiaya},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={ResLT: Residual Learning for Long-tailed Recognition}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TPAMI.2022.3174892}}
  
@article{cui2022region,
  title={Region Rebalance for Long-Tailed Semantic Segmentation},
  author={Cui, Jiequan and Yuan, Yuhui and Zhong, Zhisheng and Tian, Zhuotao and Hu, Han and Lin, Stephen and Jia, Jiaya},
  journal={arXiv preprint arXiv:2204.01969},
  year={2022}
  }