MCGCN: Multi-Correlation Graph Convolutional Network for Pedestrian Attribute Recognition-Reference-Cited by-同舟云学术

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MCGCN: Multi-Correlation Graph Convolutional Network for Pedestrian Attribute Recognition

Published:2024-03-01 Issue:3 Volume:E107.D Page:400-410
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

YU Yang¹,LIU Longlong¹,ZHU Ye¹,CEN Shixin²,LI Yang³

Affiliation:

1. School of Artificial Intelligence and Data Science, Hebei University of Technology

2. College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine

3. Information Research Institute, Tianjin Academy of Agricultural Sciences

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Link

https://www.jstage.jst.go.jp/article/transinf/E107.D/3/E107.D_2023EDP7134/_pdf

Reference47 articles.

1. [1] K. Cheng, F. Tao, Y. Zhan, M. Li, and K. Li, “Hierarchical attributes learning for pedestrian re-identification via parallel stochastic gradient descent combined with momentum correction and adaptive learning rate,” Neural Computing and Applications, vol.32, no.10, pp.5695-5712, 2020. 10.1007/s00521-019-04485-2

2. [2] H. Galiyawala and M.S. Raval, “Person retrieval in surveillance using textual query: a review,” Multimedia Tools and Applications, vol.80, no.18, pp.27343-27383, 2021. 10.1007/s11042-021-10983-0

3. [3] Z. Ji and S. Li, “Multimodal alignment and attention-based person search via natural language description,” IEEE Internet Things J., vol.7, no.11, pp.11147-11156, 2020. 10.1109/jiot.2020.2995148

4. [4] Y. Lin, L. Zheng, Z. Zheng, Y. Wu, Z. Hu, C. Yan, and Y. Yang, “Improving person re-identification by attribute and identity learning,” Pattern recognition, vol.95, pp.151-161, 2019. 10.1016/j.patcog.2019.06.006

5. [5] D. Wu, S.-J. Zheng, X.-P. Zhang, C.-A. Yuan, F. Cheng, Y. Zhao, Y.-J. Lin, Z.-Q. Zhao, Y.-L. Jiang, and D.-S. Huang, “Deep learning-based methods for person re-identification: A comprehensive review,” Neurocomputing, vol.337, pp.354-371, 2019. 10.1016/j.neucom.2019.01.079

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