This repository includes our code for the paper 'Simulating Content Consistent Vehicle Datasets with Attribute Descent'. If you want to reproduce our results, you will need to
-
Generate images by VehicleX engine using attribute descent (content level damain apation);
-
Perform style level domain adaption for generated images;
-
Train a Re-ID model for style transfered images.
The code for the first two steps is available on ./VehicleX and ./StyleDA separately.
If you find this code useful, please kindly cite:
@article{yao2019simulating,
title={Simulating Content Consistent Vehicle Datasets with Attribute Descent},
author={Yao, Yue and Zheng, Liang and Yang, Xiaodong and Naphade, Milind and Gedeon, Tom},
journal={arXiv preprint arXiv:1912.08855},
year={2019}
}
@inproceedings{tang2019pamtri,
title={Pamtri: Pose-aware multi-task learning for vehicle re-identification using highly randomized synthetic data},
author={Tang, Zheng and Naphade, Milind and Birchfield, Stan and Tremblay, Jonathan and Hodge, William and Kumar, Ratnesh and Wang, Shuo and Yang, Xiaodong},
booktitle={Proceedings of the IEEE International Conference on Computer Vision},
pages={211--220},
year={2019}
}
If you have any question, feel free to contact yue.yao@anu.edu.au