About

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

I study Normative Learning: how intelligent systems, in both their representations and their behaviors, come to approach ideals defined by task, data, and observer structure.

A central question drives this work: how can intelligent systems reliably infer the structure of the world from imperfect, multimodal observations? To address it, I study how such systems perceive ambiguous sensory inputs, selectively fuse information based on inferred causal structure, attribute prediction errors for adaptive credit assignment, and learn through closed-loop interaction with their environment.

These questions are grounded in normative and computational perspectives: asking how intelligent systems should represent uncertainty, integrate heterogeneous information, and adapt in dynamic naturalistic environments where actions continuously reshape future sensory inputs. To pursue them, I draw on optimization, statistics, and high-performance computing to develop flexible and scalable representation learning frameworks for large-scale, noisy, and unlabeled data arising in ill-posed inference problems.

Through this work, I aim to build scalable and general learning systems for computer vision, multimodal foundation models, and embodied AI — systems whose representations and behaviors converge, over learning, toward the normative ideals they are meant to realize.

Publications

Falcon: Fast Proximal Linearization of Normalized Cuts for Unsupervised Image Segmentation
Xiao Zhang, Xiangyu Han, Xiwen Lai, Yao Sun, Pei Zhang, Xia Liu, Konrad Kording
2026 The International Conference on Learning Representations (ICLR)

Rhythm Gate: Invisible Conversations in the Elevator
Xia Liu, Xiao Zhang
2025 ACM International Conference on Multimedia (MM)

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 Experience

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

• Computer Vision Research Assistant at the Chinese University of Hong Kong. (Jul. 2017 to Jul. 2019)
  Worked with Prof. Hongsheng Li and Dr. Junjie Yan

• Visiting Student at MMLab-SIAT, Chinese Academy of Sciences. (Jul. 2016 to Jul. 2017)
  Advisor: Prof. Yu Qiao

Services

I have served as a reviewer for the following conferences and journals:

Computer Vision

• ICCV: 2025, 2023, 2021, 2019
• CVPR: 2023, 2022, 2021, 2019, 2018
• ECCV: 2022, 2020

Machine Learning and AI

• ICML: 2026
• ICLR: 2026, 2025, 2024
• NeurIPS: 2024, 2023
• AISTATS: 2025

Robotics

• ICRA: 2019

Journals

• IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI)
• International Journal of Computer Vision (IJCV)
• Neurocomputing
• Computer Vision and Image Understanding (CVIU)

Teaching

Teaching assistant for the following courses at 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