Imbalanced Learning for Recognition

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

• 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);

Parametric-Contrastive-Learning

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

Overview

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.

Results and Pretrained models

Full ImageNet (Balanced setting)

Method	Model	Top-1 Acc(%)	link	log
PaCo	ResNet-50	79.3	download	download
PaCo	ResNet-101	80.9	download	download
PaCo	ResNet-200	81.8	download	download

ImageNet-LT (Imbalance setting)

Method	Model	Top-1 Acc(%)	link	log
PaCo	ResNet-50	57.0	download	download
PaCo	ResNeXt-50	58.2	download	download
PaCo	ResNeXt-101	60.0	download	download

iNaturalist 2018 (Imbalanced setting)

Method	Model	Top-1 Acc(%)	link	log
PaCo	ResNet-50	73.2	TBD	download
PaCo	ResNet-152	75.2	TBD	download

Places-LT (Imbalanced setting)

Method	Model	Top-1 Acc(%)	link	log
PaCo	ResNet-152	41.2	TBD	download

Get Started

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

Contact

If you have any questions, feel free to contact us through email (jiequancui@link.cuhk.edu.hk) or Github issues. Enjoy!

BibTex

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

@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}
  }