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Jie Cao

             E-mail: jie.cao@cripac.ia.ac.cn
             Tel:010-82544489
             Address:Room 1509, Intelligent Building, 95 Zhongguancun East Road, Haidian District, BEIJING, CHINA
             Postcode: 100190
             Homepage: http://cripac.ia.ac.cn/en/EN/column/item150.shtml

Overview

Dr. Cao received the BSc degree in Automation from North China Electric Power University and the Ph.D. degree in Pattern Recognition and Intelligent Systems from the Institute of Automation, Chinese Academy of Sciences in 2016 and 2021, respectively. Since July 2021, Dr. Cao has joined CRIPAC, where he is currently an assistant professor. He is currently a member of China Society of Image and Graphics (2021- ). He has published several articles in highly ranked international journals, such as IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), IEEE Trans. on Knowledge and Data Engineering (TIFS), and leading international conferences, such as NeurIPS, CVPR, AAAI, IJCAI, and ECCV.

Research Area

Computer vision, biometrics, face preprocessing

Selected Publications

1.Jie Cao, Yibo Hu, Hongwen Zhang, Ran He, and Zhenan Sun, “Towards High Fidelity Face Frontalization in the Wild,” International Journal of Computer Vision (IJCV), vol. 128, pp. 1485–1504, 2020.
2.Jie Cao, Yibo Hu, Bing Yu, Ran He, and Zhenan Sun, “3D Aided Duet GANs for Multi-View Face Image Synthesis,” IEEE Transactions on Information Forensics and Security (TIFS), vol. 14, no. 8, pp. 2028–2042, 2019.
3.Jie Cao, Yibo Hu, Hongwen Zhang, Ran He, and Zhenan Sun, “Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization,” in Advances in Neural Information Processing Systems (NeurIPS), 2018, pp. 2867–2877.
4.Jie Cao, Huaibo Huang, Yi Li, Ran He, and Zhenan Sun, “Informative Sample Mining Network for Multi-Domain Image-to-Image Translation,” in European Conference on Computer Vision (ECCV), 2020, pp. 404–419.
5.Jie Cao, Luanxuan Hou, Ming-Hsuan Yang, Ran He, and Zhenan Sun, “ReMix: Towards Image-to-Image Translation with Limited Data,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 15 018–15 027.

 
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