@article{zheng2019joint,
title={Joint discriminative and generative learning for person re-identification},
author={Zheng, Zhedong and Yang, Xiaodong and Yu, Zhiding and Zheng, Liang and Yang, Yi and Kautz, Jan},
journal={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2019}
}
random erasing的正则效果:
@article{zhong2017random,
title={Random erasing data augmentation},
author={Zhong, Zhun and Zheng, Liang and Kang, Guoliang and Li, Shaozi and Yang, Yi},
journal={arXiv preprint arXiv:1708.04896},
year={2017}
}
svdnet 和 triplet net 提出加入 bn层:
@article{DBLP:journals/corr/SunZDW17,
author = {Yifan Sun and
Liang Zheng and
Weijian Deng and
Shengjin Wang},
title = {SVDNet for Pedestrian Retrieval},
booktitle = {ICCV},
year = {2017},
}
@article{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Hermans, Alexander and Beyer, Lucas and Leibe, Bastian},
journal={arXiv preprint arXiv:1703.07737},
year={2017}
}
基础的网络结构:
@article{zheng2018discriminatively,
title={A discriminatively learned CNN embedding for person reidentification},
author={Zheng, Zhedong and Zheng, Liang and Yang, Yi},
journal={ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)},
volume={14},
number={1},
pages={13},
year={2018},
publisher={ACM}
}
你好。这个baseline结合了挺多论文的。
结果的话,你可以引用
random erasing的正则效果:
svdnet 和 triplet net 提出加入 bn层:
基础的网络结构: