LT-DS

[ECCV 2022] Repo for our paper "Tackling Long-Tailed Category Distribution Under Domain Shifts"

[project] [dataset]

Dataset

We provided two datasets for benchmarking LT-DS problems. Due to the license issue, we only provided instructions on how to create the corresponding datasets. Please follow here.

Training

AWA2-LTS

python train/trainer.py --cfg config/exp/awa2.yaml 

ImageNet-LTS

python train/trainer.py --cfg config/exp/imagenet.yaml

TODO

• Add evaluation scripts
• Add requirements
• Add PACS-ODG experiments
• Add Imbalanced Baselines

Citation

If you find our paper useful, please consider citing:

@inproceedings{gu2022tackling,
  title={Tackling Long-Tailed Category Distribution Under Domain Shifts},
  author={Gu, Xiao and Guo, Yao and Li, Zeju and Qiu, Jianing and Dou, Qi and Liu, Yuxuan and Lo, Benny and Yang, Guang-Zhong},
  booktitle={ECCV},
  year={2022}

Acknowledgement

Our codes are inspired from the following repos: OpenDG-DAMLBagofTricks-LT ISDA.