[ECCV 2022] Repo for our paper "Tackling Long-Tailed Category Distribution Under Domain Shifts"
Machine learning models fail to perform well on real-world applications when
- LT: the category distribution P(Y) of the training dataset suffers from long-tailed distribution;
- DS: the test data is drawn from different conditional distributions P(X|Y).
Existing approaches cannot handle the scenario where both issues exist, which however is common for real-world applications. In this study, we took a step forward and looked into the problem of long-tailed classification under domain shifts. By taking both the categorical distribution bias and conditional distribution shifts into account, we designed three novel core functional blocks including Distribution Calibrated Classification Loss, Visual-Semantic Mapping and Semantic-Similarity Guided Augmentation. Furthermore, we adopted a meta-learning framework which integrates the three blocks to improve domain generalization on unseen target domains.
We provide two datasets for benchmarking LT-DS (Long-Tailed Under Domain shifts) algorithms. Due to the license issue, we only provided instructions on how to create the corresponding datasets. Please follow here.
conda env create -f requirement.yml
python train/trainer.py --cfg config/exp/awa2.yaml DATA.SOURCE SHUV DATA.TARGET SHUVO
python train/trainer.py --cfg config/exp/awa2.yaml DATA.SOURCE SHUV DATA.TARGET SHUVO MODE test
python train/trainer.py --cfg config/exp/imagenet.yaml DATA.SOURCE SHUV DATA.TARGET SHUVO
python train/trainer.py --cfg config/exp/imagenet.yaml DATA.SOURCE SHUV DATA.TARGET SHUVO MODE test
- Add requirements
- Add evaluation scripts
- Add imbalanced baselines
If you find our paper/code 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}
}Our codes are inspired by the following repos: [OpenDG-DAML] [BagofTricks-LT ] [ISDA].