Bo Ding (丁博)

I am a first-year Ph.D. student in Electrical Engineering at the City University of Hong Kong (CityU), supervised by Professor Haoliang Li. Before CityU, I received my master's degree (2021-2024) at the Institute of Computing Technology, Chinese Academy of Sciences (ICT, CAS) supervised by Professor Shihong Xia, researching Face Forgery Detection and Talking Face Generation.

My research interests include:

  • Applications: AI for Digital Human, AI for Security
  • Technologies: Generative Model, Domain Generalization

Please don't hesitate to contact me if you are interested in my research.
Contact: bo.ding [AT] my.cityu.edu.hk

Currículum Vitae    /    GitHub    /    Google Scholar

News
2024/08 I entered the City University of Hong Kong as a Ph.D. student.
2024/05 Completed my master's theis defense.
2024/04 Invited to serve as a Reviewer for ACM MM 2024.
2024/03 Invited to serve as a PC member for WWW 2024.
2023/07 One first-authored paper got accepted by MTAP.
2023/06 One co-authored paper got accepted.
Publications
MyPortrait: Morphable Prior-Guided Personalized Portrait Generation
Bo Ding, Zhenfeng Fan, Shuang Yang, and Shihong Xia
Preprint
TL;DR: We present Myportrait, a method for learning personalized neural portraits. By incorporating personalized prior from a monocular video and morphable prior from 3DMM, our framework can generate realistic portraits with personalized details.
Mining Collaborative Spatio-Temporal Clues for Face Forgery Detection
Bo Ding, Zhenfeng Fan, Zejun Zhao, and Shihong Xia
Multimedia Tools and Applications (JCR Q2, CCF-C), 2023
Paper  /  Chinese Blog
TL;DR: We propose a multi-branch spatio-temporal difference network for face forgery detection by capturing complementary low-level spatio-temporal features in videos, which can enhance the generalization ability of the model.
Deepfake Detection Based on Incremental Learning
Zejun Zhao, Zhenfeng Fan, Bo Ding, and Shihong Xia
Frontiers of Data and Computing (In Chinese), 2023
Paper
TL;DR: We design a face forgery detection system based on incremental learning, which reduces the training cost of the model when introducing new forgery samples.
GT-GAN: A General Transductive Zero-Shot Learning Method Based on GAN
Junhao Dong, Bo Xiao, Bo Ding, and Haoyv Wang
IEEE Access (JCR Q2), 2020
Paper
TL;DR: We propose a general transductive ZSL method based on GANs, called GT-GAN, which introduces unlabeled unseen samples for training, to improve the performance of the generator.
Education
2024-Present Ph.D. in Electrical Engineering
Department of Electrical Engineering
City University of Hong Kong
2021-2024 M.S. in Computer Science and Technology
University of Chinese Academy of Sciences
Cultivation Unit: Institute of Computing Technology, Chinese Academy of Sciences
2017-2021 B.Eng. in Communication Engineering
School of Information and Communication Engineering
Beijing University of Posts and Telecommunications
Honors and Awards
2024 Excellent Prize of the President Scholarship, ICT, CAS
2023 The First Prize Scholarship, ICT, CAS
2022, 2023 Outstanding Student Leader, ICT, CAS
2022, 2023, 2024 Merit Student, ICT, CAS
2021 Excellent Bachelor Thesis, BUPT
2020 Honorable Mention, Mathematical Contest In Modeling
2019 Second Prize, The Chinese Mathematics Competitions
2019 The Enterprise Scholarship, BUPT
2018, 2020 National Encouragement Scholarship, BUPT