TY - GEN
T1 - Dynamically Transformed Instance Normalization Network for Generalizable Person Re-Identification
AU - Jiao, Bingliang
AU - Liu, Lingqiao
AU - Gao, Liying
AU - Lin, Guosheng
AU - Yang, Lu
AU - Zhang, Shizhou
AU - Wang, Peng
AU - Zhang, Yanning
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Existing person re-identification methods often suffer significant performance degradation on unseen domains, which fuels interest in domain generalizable person re-identification (DG-PReID). As an effective technology to alleviate domain variance, the Instance Normalization (IN) has been widely employed in many existing works. However, IN also suffers from the limitation of eliminating discriminative patterns that might be useful for a particular domain or instance. In this work, we propose a new normalization scheme called Dynamically Transformed Instance Normalization (DTIN) to alleviate the drawback of IN. Our idea is to employ dynamic convolution to allow the unnormalized feature to control the transformation of the normalized features into new representations. In this way, we can ensure the network has sufficient flexibility to strike the right balance between eliminating irrelevant domain-specific features and adapting to individual domains or instances. We further utilize a multi-task learning strategy to train the model, ensuring it can adaptively produce discriminative feature representations for an arbitrary domain. Our results show a great domain generalization capability and achieve state-of-the-art performance on three mainstream DG-PReID settings.
AB - Existing person re-identification methods often suffer significant performance degradation on unseen domains, which fuels interest in domain generalizable person re-identification (DG-PReID). As an effective technology to alleviate domain variance, the Instance Normalization (IN) has been widely employed in many existing works. However, IN also suffers from the limitation of eliminating discriminative patterns that might be useful for a particular domain or instance. In this work, we propose a new normalization scheme called Dynamically Transformed Instance Normalization (DTIN) to alleviate the drawback of IN. Our idea is to employ dynamic convolution to allow the unnormalized feature to control the transformation of the normalized features into new representations. In this way, we can ensure the network has sufficient flexibility to strike the right balance between eliminating irrelevant domain-specific features and adapting to individual domains or instances. We further utilize a multi-task learning strategy to train the model, ensuring it can adaptively produce discriminative feature representations for an arbitrary domain. Our results show a great domain generalization capability and achieve state-of-the-art performance on three mainstream DG-PReID settings.
KW - Domain generalization
KW - Dynamic convolution
KW - Instance Normalization
KW - Person re-identification
UR - http://www.scopus.com/inward/record.url?scp=85142725802&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-19781-9_17
DO - 10.1007/978-3-031-19781-9_17
M3 - 会议稿件
AN - SCOPUS:85142725802
SN - 9783031197802
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 285
EP - 301
BT - Computer Vision – ECCV 2022 - 17th European Conference, Proceedings
A2 - Avidan, Shai
A2 - Brostow, Gabriel
A2 - Cissé, Moustapha
A2 - Farinella, Giovanni Maria
A2 - Hassner, Tal
PB - Springer Science and Business Media Deutschland GmbH
T2 - 17th European Conference on Computer Vision, ECCV 2022
Y2 - 23 October 2022 through 27 October 2022
ER -