南京大学学报(自然科学版) ›› 2021, Vol. 57 ›› Issue (5): 715–723.doi: 10.13232/j.cnki.jnju.2021.05.001
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基于域分离和对抗学习的跨域行人重识别
薛峰1,2, 李凡1,2(
), 李爽1,2, 李华锋1,2
- 1.昆明理工大学信息工程与自动化学院，昆明，650500
2.云南省人工智能重点实验室，昆明理工大学，昆明，650500
Cross⁃domain person re⁃identification based on domain separation and adversarial learning
Feng Xue1,2, Fan Li1,2(), Shuang Li1,2, Huafeng Li1,2
- 1.Faculty of Information Engineering and Automation, Kunming University of Science and Technology，Kunming，650500, China
2.Yunnan Key Laboratory of Artificial Intelligence，Kunming University of Science and Technology，Kunming，650500，China
摘要：
针对跨域行人重识别应用中源域与目标域差异较大、现有模型无法在剥离域信息的同时有效获取关键身份信息的问题，提出一种基于对抗学习分离图像域信息与身份信息的方法.该方法由域分离和对抗学习两个阶段构成：域分离阶段分离图像行人特征和域特征；对抗学习阶段通过特征提取器与相机分类器的对抗学习，提升模型对域信息与身份信息的区分能力.在Market?1501，DukeMTMC?reID和MSMT17数据集上开展跨域行人重识别验证实验，实验结果表明，所提方法在跨域行人重识别任务上取得了显著的性能提升.
中图分类号:
- TP391.41
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