TY - GEN
T1 - Camera Style Guided Feature Generation for Person Re-identification
AU - Hu, Hantao
AU - Liu, Yang
AU - Lv, Kai
AU - Zheng, Yanwei
AU - Zhang, Wei
AU - Ke, Wei
AU - Sheng, Hao
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Camera variance has always been a troublesome matter in person re-identification (re-ID). Recently, more and more interests have grown in alleviating the camera variance problem by data augmentation through generative models. However, these methods, mostly based on image-level generative adversarial networks (GANs), require huge computational power during the training process of generative models. In this paper, we propose to solve the person re-ID problem by adopting a feature level camera-style guided GAN, which can serve as an intra-class augmentation method to enhance the model robustness against camera variance. Specifically, the proposed method makes camera-style transfer on input features while preserving the corresponding identity information. Moreover, the training process can be directly injected into the re-ID task in an end-to-end manner, which means we can deploy our methods with much less time and space costs. Experiments show the validity of the generative model and its benefits towards re-ID performance on Market-1501 and DukeMTMC-reID datasets.
AB - Camera variance has always been a troublesome matter in person re-identification (re-ID). Recently, more and more interests have grown in alleviating the camera variance problem by data augmentation through generative models. However, these methods, mostly based on image-level generative adversarial networks (GANs), require huge computational power during the training process of generative models. In this paper, we propose to solve the person re-ID problem by adopting a feature level camera-style guided GAN, which can serve as an intra-class augmentation method to enhance the model robustness against camera variance. Specifically, the proposed method makes camera-style transfer on input features while preserving the corresponding identity information. Moreover, the training process can be directly injected into the re-ID task in an end-to-end manner, which means we can deploy our methods with much less time and space costs. Experiments show the validity of the generative model and its benefits towards re-ID performance on Market-1501 and DukeMTMC-reID datasets.
KW - Adaptive batch normalization
KW - Camera-style guided
KW - Feature generation
KW - Generative adversarial networks
KW - Person re-identification
UR - http://www.scopus.com/inward/record.url?scp=85091499337&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-59016-1_14
DO - 10.1007/978-3-030-59016-1_14
M3 - Conference contribution
AN - SCOPUS:85091499337
SN - 9783030590154
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 158
EP - 169
BT - Wireless Algorithms, Systems, and Applications - 15th International Conference, WASA 2020, Proceedings
A2 - Yu, Dongxiao
A2 - Dressler, Falko
A2 - Yu, Jiguo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 15th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2020
Y2 - 13 September 2020 through 15 September 2020
ER -