Volume 31 Issue 1
Jan.  2022
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FU Lihua, DU Yubin, DING Yu, WANG Dan, JIANG Hanxu, ZHANG Haitao. Domain Adaptive Learning with Multi-Granularity Features for Unsupervised Person Re-identification[J]. Chinese Journal of Electronics, 2022, 31(1): 116-128. doi: 10.1049/cje.2020.00.072
Citation: FU Lihua, DU Yubin, DING Yu, WANG Dan, JIANG Hanxu, ZHANG Haitao. Domain Adaptive Learning with Multi-Granularity Features for Unsupervised Person Re-identification[J]. Chinese Journal of Electronics, 2022, 31(1): 116-128. doi: 10.1049/cje.2020.00.072

Domain Adaptive Learning with Multi-Granularity Features for Unsupervised Person Re-identification

doi: 10.1049/cje.2020.00.072
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  • Author Bio:

    (corresponding author) received the Ph.D. degree in computer science from Northwestern Polytechnical University in 2005. She is currently an Associate Professor with Beijng University of Technology. Her current research interests include image processing and computer vision. (Email: fulh@bjut.edu.cn)

    received the B.S. degree from Shandong University of Technology in 2018. He is currently pursuing the master’s degree with Beijng University of Technology. His current research interests include image processing and computer vision

    received the B.S. degree from Henan University of Science and Technology in 2016. He is currently pursuing the master’s degree with Beijng University of Technology. His current research interests include image processing and computer vision

    received the Ph.D. degree in computer science from Northeastern University in 2003. She is currently a Professor with Beijng University of Technology. Her current research interests include image processing and computer vision

    received the B.S. degree from Beijng University of Technology in 2019. He is currently pursuing the master’s degree with Beijng University of Technology. His current research interests include image processing and computer vision

    received the B.S. degree from Huzhou University in 2019. He is currently pursuing the master’s degree with Beijng University of Technology. His current research interests include image processing and computer vision

  • Received Date: 2020-03-09
  • Accepted Date: 2020-11-13
  • Available Online: 2021-09-23
  • Publish Date: 2022-01-05
  • Unsupervised person re-identification (Re-ID) aims to improve the model’s scalability and obtain better Re-ID results in the unlabeled data domain. In this paper, we propose an unsupervised person Re-ID method based on multi-granularity feature representation and domain adaptive learning, which can effectively improve the performance of unsupervised person re-identification. The multi-granularity feature extraction module integrates global and local information of different granularity to obtain the multi-granularity person feature representation with rich discriminative information. The source domain classification module learns the labeled source dataset classification and obtains the person’s discriminative knowledge in the source domain. On this basis, the domain adaptive module further considers the difference between the target domain and the source domain to learn adaptively for the model. Experiments on multiple public datasets show that the proposed method can achieve a competitive performance among other state-of-the-art unsupervised Re-ID methods.
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