Prof. Antoni B. Chan
Associate Head, Dept. of Computer Science
Deputy Director, Multimedia Engineering Research Centre (MERC)
BSc MEng Cornell, PhD UC San Diego

Video, Image, and Sound Analysis Lab (VISAL)
Department of Computer Science
City University of Hong Kong

Office: Room AC1-G7311, Yeung Kin Man Academic Building (lift 7)
Phone: +852 3442 6509
Fax: +852 3442 0503
Email: abchan at cityu dot edu dot hk


Dr. Antoni Chan is a Professor at the City University of Hong Kong in the Department of Computer Science.  Before joining CityU, he was a postdoctoral researcher in the Department of Electrical and Computer Engineering at the University of California, San Diego (UC San Diego).  He received the Ph.D. degree from UC San Diego in 2008 studying in the Statistical and Visual Computing Lab (SVCL). He received the B.Sc. and M.Eng. in Electrical Engineering from Cornell University in 2000 and 2001. From 2001 to 2003, he was a Visiting Scientist in the Computer Vision and Image Analysis lab at Cornell. In 2005, he was a summer intern at Google in New York City. In 2012, he was the recipient of an Early Career Award from the Research Grants Council of the Hong Kong SAR, China.

Research Interests [more]

Computer Vision, Surveillance, Machine Learning, Pattern Recognition, Computer Audition, Music Information Retrieval, Eye Gaze Analysis

dynamic textures, motion segmentation, motion analysis, semantic image annotation, image retrieval, crowd counting, probabilistic graphical models, support vector machines, Bayesian regression, Gaussian processes, semantic music annotation and retrieval, music segmentation, feature extraction.

  • For more information about my current research projects, please visit my lab website.
  • Opportunities for graduate students and research assistants! If you are interested in joining the lab, please check this information. Outstanding non-HK students may also consider applying for the HK PhD fellowship.
  • RA/Postdoc position is available here.
  • My collaborator has a postdoc position available on Explainable AI (XAI).

Recent Publications [more]

  • Improved Fine-Tuning by Better Leveraging Pre-Training Data.
    Ziquan Liu, Yi Xu, Yuanhong Xu, Qi Qian, Hao Li, Xiangyang Ji, Antoni B. Chan, and Rong Jin,
    In: Neural Information Processing Systems (NeurIPS), To appear 2022.
  • Boosting Adversarial Robustness From The Perspective of Effective Margin Regularization.
    Ziquan Liu and Antoni B. Chan,
    In: British Machine Vision Conference, to appear 2022.
  • Scale-Prior Deformable Convolution for Exemplar-Guided Class-Agnostic Counting.
    Wei Lin, Kunlin Yang, Xinzhu Ma, Junyu Gao, Lingbo Liu, Shinan Liu, Jun Hou, Shuai Yi, and Antoni B. Chan,
    In: British Machine Vision Conference, to appear 2022.
  • Calibration-free Multi-view Crowd Counting.
    Qi Zhang and Antoni B. Chan,
    In: European Conference on Computer Vision (ECCV), Tel Aviv, to appear Oct 2022.
  • 3D Crowd Counting via Geometric Attention-guided Multi-View Fusion.
    Qi Zhang and Antoni Bert Chan,
    International Journal of Computer Vision (IJCV), to appear 2022.
  • RegGeoNet: Learning Regular Representations for Large-Scale 3D Point Clouds.
    Qijian Zhang, Junhui Hou, Yue Qian, Antoni B. Chan, Juyong Zhang, and Ying He,
    International Journal of Computer Vision (IJCV), to appear 2022.
  • On Becoming Socially Anxious: Toddlers’ Attention Bias to Fearful Faces.
    Lamei Wang, Janet H. Hsiao, Antoni B. Chan, Jasmine Cheung, San Hung, and Terry Kit-fong Au,
    Developmental Psychology, to appear 2022.
  • Understanding the role of eye movement consistency in face recognition and autism through integrating deep neural networks and hidden Markov models.
    Janet H. Hsiao, Jeehye An, Veronica Kit Sum Hui, Yueyuan Zheng, and Antoni B. Chan,
    npj Science of Learning, to appear 2022.
  • Asymptotic Optimality for Active Learning Processes.
    Xueying Zhan, Yaowei Wang, and Antoni B. Chan,
    In: Uncertainty in Artificial Intelligence (UAI), to appear Aug 2022.
  • Bits-Ensemble: Towards Light-Weight Robust Deep Ensemble by Bits-Sharing.
    Yufei Cui, Shangyu Wu, Qiao Li, Antoni B. Chan, Tei-Wei Kuo, and Jason Xue Chun,
    IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD) (accepted to CASES 2022), to appear 2022.
  • PRIMAL-GMM: PaRametrIc MAnifold Learning of Gaussian Mixture Models.
    Ziquan Liu, Lei Yu, Janet H. Hsiao, and Antoni B. Chan,
    IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 44(6):3197-3211, June 2022 (online 2021). [code]
  • Crowd Counting in the Frequency Domain.
    Weibo Shu, Jia Wan, Kay Chen Tan, Sam Kwong, and Antoni B. Chan,
    In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2022.
  • Understanding children’s attention to dental caries through eye-tracking.
    Vanessa Y. Cho, Janet H. Hsiao, Antoni B. Chan, Hien C. Ngo, Nigel M. King, and Robert P. Anthonappa,
    Caries Research, 56(2):129-137, June 2022.
  • Wide-Area Crowd Counting: Multi-View Fusion Networks for Counting in Large Scenes.
    Qi Zhang and Antoni B. Chan,
    International Journal of Computer Vision (IJCV), 130(8):1938-1960, May 2022.
  • Eye movement analysis of children's attention for midline diastema.
    Vanessa Y. Cho, Janet H. Hsiao, Antoni B. Chan, Hien C. Ngo, Nigel M. King, and Robert P. Anthonappa,
    Scientific Reports, 12:7462, May 2022.

Selected Publications [more]

Google Scholar Google Scholar
Microsoft Academic Microsoft Academic
Scopus ID: 14015159100

Recent Project Pages [more]

Dynamic Momentum Adaptation for Zero-Shot Cross-Domain Crowd Counting

We propose a novel Crowd Counting framework built upon an external Momentum Template, termed C2MoT, which enables the encoding of domain specific information via an external template representation.

Group-based Distinctive Image Captioning with Memory Attention

We improve the distinctiveness of image captions using a Group-based Distinctive Captioning Model (GdisCap), which compares each image with other images in one similar group and highlights the uniqueness of each image.

Hierarchical Learning of Hidden Markov Models with Clustering Regularization

We propose a novel tree structure variational Bayesian method to learn the individual model and group model simultaneously by treating the group models as the parents of individual models, so that the individual model is learned from observations and regularized by its parents, and conversely, the parent model will be optimized to best represent its children.

Chinese White Dolphin Detection in the Wild

To reduce the human experts’ workload and improve the observation
accuracy, in this paper, we develop a practical system to detect Chinese White Dolphins in the wild automatically.

Eye Movement analysis with Hidden Markov Models (EMHMM) with co-clustering

We analyze eye movement data on stimuli with different feature layouts. Through co-clustering HMMs, we discover common strategies on each stimuli and cluster subjects with similar strategies.

Recent Datasets and Code [more]

Dolphin-14k: Chinese White Dolphin detection dataset

A dataset consisting of  Chinese White Dolphin (CWD) and distractors for detection tasks.

Crowd counting: Zero-shot cross-domain counting

Generalized loss function for crowd counting.

CVCS: Cross-View Cross-Scene Multi-View Crowd Counting Dataset

Synthetic dataset for cross-view cross-scene multi-view counting. The dataset contains 31 scenes, each with about ~100 camera views. For each scene, we capture 100 multi-view images of crowds.

Crowd counting: Generalized loss function

Generalized loss function for crowd counting.

Fine-Grained Crowd Counting Dataset

Dataset for fine-grained crowd counting, which differentiates a crowd into categories based on the low-level behavior attributes of the individuals (e.g. standing/sitting or violent behavior) and then counts the number of people in each category.


  • CS 4487 – Machine Learning (undergraduate) — 2015A-2018A.
  • CS 5487 – Machine Learning: Principles & Practice (postgraduate) — 2012A-2022B.
  • CS 5489 – Machine Learning: Algorithms & Applications (postgraduate) — 2020B-2021A.
  • CS 6487 – Topics in Machine Learning (postgraduate) — 2019B.
  • GE 2326 – Probability in Action: From the Unfinished Game to the Modern World — 2015B-2017B.
  • GE 1319 – Interdisciplinary Research for Smart Professionals — 2013B-2017B.
  • CS 5301 – Computer Programming — 2012A-2014A.
  • CS 2363 – Computer Programming — 2009A-2011A.
  • CS 3306 (B) – Contemporary Programming Methods in Java — 2010B.
  • CS 4380 (B) – Web 2.0 Technologies — 2011B, 2012B.
  • Multimedia Subject Group leader
  • Research Mentoring Scheme Coordinator
  • Final Year Project Coordinator (2016-2022)
  • MSCS Project and Guided Study Coordinator (2016-2022)
  • BScCM Deputy Programme Leader (2020-2022)


  • Action Editor, Transactions on Machine Learning Research (2022-now)
  • Senior Area Editor, IEEE Signal Processing Letters (2016-2020)
  • Associate Editor, IEEE Signal Processing Letters (2014-2016)
  • Conference Area Chair
    • CVPR – 2020
    • ICCV – 2015, 2017, 2019, 2021
    • ECCV – 2022
    • NeurIPS – 2020, 2021, 2022
    • ICML – 2021, 2022
    • ICLR – 2021, 2023
    • ICPR – 2020
    • Pacific Graphics – 2018
  • Conference Senior PC
    • AAAI – 2021, 2022
    • IJCAI – 2019-20
  • Conference Program Committees
    • CVPR – 2012-2019, 2021, 2022
    • ICCV – 2011, 2013
    • ECCV – 2012, 2014, 2016, 2018
    • ACCV – 2011, 2014, 2016
    • ICML – 2012, 2013, 2014, 2015, 2018, 2019, 2020
    • NIPS – 2015, 2017, 2018, 2019
    • ICLR – 2022
    • Siggraph (tertiary)- 2018
  • Journal Reviewing
    • IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI)
    • IEEE Trans. on Image Processing (TIP)
    • Intl. Journal Computer Vision (IJCV)
    • IEEE Trans. on Circuits and Systems for Video Technology (TCSVT)
    • IEEE Trans. on Neural Networks (TNN)
    • IEEE Trans. on Multimedia
    • IEEE Trans. Intelligent Transportation Systems
  • Organized Events
    • CogSci 2022 Hong Kong Meetup & Symposium: Computational Approaches to Psychological Research, Aug 2022.

Awards and Honors

  • Top 2% Most Highly Cited Researchers (Ioannidis et al. 2019. Plos Biology)
  • The President’s Award, City University of Hong Kong, 2016.
  • Early Career Award, Research Grants Council of Hong Kong, 2012.
  • NSF IGERT Fellowship: Vision and Learning in Humans and Machines, UCSD, 2006-07.
  • Outstanding Teaching Assistant Award, ECE Department, UCSD, 2005-06.
  • Office of the President Award, UCSD, 2003.
  • Henry G. White Scholorship, Cornell University, 2001.
  • Knauss M. Engineering Scholorship, Cornell University, 2001.
  • GTE Fellowship, Cornell University, 2001.
Mailing Address:

Prof. Antoni Chan,
Department of Computer Science,
City University of Hong Kong,
Tat Chee Avenue,
Kowloon Tong, Hong Kong.

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