About

I am currently a postdoc scholar at the University of Pennsylvania, advised by Prof. Konrad Kording. Perviously I obtained my Ph.D. degree from Multimedia Lab (MMLab) at the Chinese University of Hong Kong, supervised by Prof. Xiaogang Wang and Prof. Hongsheng Li. I have also collaborated with researchers in Nvidia, SenseTime, and the Chinese Academy of Sciences (CAS). Prior to CUHK, I received my B.Eng degree from College of Intelligence and Computing, Tianjin University (TJU) in 2017. At that time, I also minored in Finance.

My research focuses on advancing the development of scalable AI perception systems that can effectively address diverse machine learning issues in naturalistic settings. I am keen on exploring robust representation learning pipelines that are both flexible and powerful. This entails a comprehensive study of general representation employing methods such as optimization, statistics, and HPC systems to overcome challenges presented by defective data (large-scale, unlabeled, and ill-posed). My current interest lies in several representation learning areas including generative data augmentation, self-supervision pretraining, modality alignment of foundation model, and vision learning with parameter-efficient adaptation (PETL). Additionally, I am also interested in multi-view fused perception, and high-performance real-time AI system.

Publications

Refining Pseudo Labels with Clustering Consensus over Generations for Unsupervised Object Re-Identification
Xiao Zhang, Yixiao Ge, Yu Qiao, Hongsheng Li
2021 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

RBF-Softmax: Learning Deep Representative Prototypes with Radial Basis Function Softmax
Xiao Zhang, Rui Zhao, Yu Qiao, Hongsheng Li
2020 European Conference on Computer Vision (ECCV)

Adacos: Adaptively Scaling Cosine Logits for effectively Learning Deep Face Representations
Xiao Zhang, Rui Zhao, Yu Qiao, Xiaogang Wang, Hongsheng Li
2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [Oral]

P2SGrad: Refined Gradients for Optimizing Deep Face Models
Xiao Zhang, Rui Zhao, Junjie Yan, Mengya Gao, Yu Qiao, Xiaogang Wang, Hongsheng Li
2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Range Loss for Deep Face Recognition with Long-tailed Training Data
Xiao Zhang, Zhiyuan Fang, Yandong Wen, Zhifeng Li, Yu Qiao
2017 IEEE International Conference on Computer Vision (ICCV)

Research Experiences

• Research Intern at Nvidia. ((Jul. 2021 to Jul. 2022))
  Worked on self-supervised representation learning with Dr. Charles Cheung

• Computer Vision Research Intern at SenseTime Research. (Jul. 2017 to Jul. 2019)
  Worked on large-scale face recognition with Dr. Rui Zhao and Dr. Junjie Yan

• Visiting student at MMLab, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. (Jul. 2016 to Jul. 2017)
  Advisor: Prof. Yu Qiao

Academic Services

Reviewer of the following conferences and journals:

• ICLR2024, NeurIPS2023, ICCV2023, CVPR2023, ECCV2022, CVPR2022, ICCV2021, CVPR2021, ECCV2020, ICCV2019, ICRA2019, CVPR2019, CVPR2018
• T-PAMI, IJCV, Neurocomputing, CVIU

Teaching

Teaching Assistant of the following courses in CUHK:

• CUHK ENGG 5202, Pattern Recognition, Spring 2022
• CUHK ELEG 5760, Machine Learning for Signal Processing Applications, Fall 2019.
• CUHK ENGG 2030, Signals and Systems, Fall 2022, Fall 2021, Spring 2021, Fall 2020, Spring 2020, Fall 2019