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

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

News

  • Call for Papers: Special Issue on “Applications of artificial intelligence, computer vision, physics and econometrics modelling methods in pedestrian traffic modelling and crowd safety” in Transportation Research Part C: Emerging Technologies. Deadline April 30th, 2023.
  • RA/Postdoc position is available here.
  • My collaborator has a postdoc position available on Explainable AI (XAI).

Bio

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.

Recent Publications [more]

  • ODAM: Gradient-based Instance-Specific Visual Explanations for Object Detection.
    Chenyang Zhao and Antoni B. Chan,
    In: Intl. Conf. on Learning Representations (ICLR), Rwanda, to appear May 2023.
  • Bayes-MIL: A New Probabilistic Perspective on Attention-based Multiple Instance Learning for Whole Slide Images.
    Yufei Cui, Ziquan Liu, Xiangyu Liu, Xue Liu, Cong Wang, Tei-Wei Kuo, Jason Xue Chun, and Antoni B. Chan,
    In: Intl. Conf. on Learning Representations (ICLR), Rwanda, to appear May 2023.
  • Variational Nested Dropout.
    Yufei Cui, Yu Mao, Ziquan Liu, Qiao Li, Antoni B. Chan, Xue Liu, Tei-Wei Kuo, and Xue Chun,
    IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), to appear 2023.
  • Optimal planning of municipal-scale distributed rooftop photovoltaic systems with maximized solar energy generation under constraints in high-density cities.
    Haoshan Ren, Zhenjun Ma, Antoni B. Chan, and Yongjun Sun,
    Energy, 263(Part A):125686, Jan 2023.
  • 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.
  • An Empirical Study on Distribution Shift Robustness From the Perspective of Pre-Training and Data Augmentation.
    Ziquan Liu, Yi Xu, Yuanhong Xu, Qi Qian, Hao Li, Rong Jin, Xiangyang Ji, and Antoni B. Chan,
    In: NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and Applications (DistShift), to appear 2022.
  • Precise Augmentation and Counting of Helicobacter Pylori in Histology Image.
    Yufei Cui, Yixin Chen, Zhifeng Shuai, Fang Peng, Yanbo Lv, Luoning Zheng, Xue Liu, Antoni B. Chan, Tei-Wei Kuo, and Chun Jason Xue,
    In: NeurIPS 2022 Workshop on Medical Imaging meets NeurIPS (MedNeurIPS), 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.
  • 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), 41(11):4397-4408, Nov 2022 (CASES 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, 7:28, Oct 2022.
  • Calibration-free Multi-view Crowd Counting.
    Qi Zhang and Antoni B. Chan,
    In: European Conference on Computer Vision (ECCV), Tel Aviv, Oct 2022. [supplemental]
  • Understanding children's attention to traumatic dental injuries using eye-tracking.
    Vanessa Y. Cho, Janet H. Hsiao, Antoni B. Chan, Hien C. Ngo, Nigel M. King, and Robert P. Anthonappa,
    Dental Traumatology, 38(5):410-416, Oct 2022.
  • 3D Crowd Counting via Geometric Attention-guided Multi-View Fusion.
    Qi Zhang and Antoni B. Chan,
    International Journal of Computer Vision (IJCV), 130:3123-3139, Sep 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.

Selected Publications [more]

Google Scholar Google Scholar
Microsoft Academic Microsoft Academic
ORCID orcid.org/0000-0002-2886-2513
Scopus ID: 14015159100

Recent Project Pages [more]

Calibration-free Multi-view Crowd Counting

We propose a calibration-free multi-view crowd counting (CF-MVCC) method, which obtains the scene-level count as a weighted summation over the predicted density maps from the camera-views, without needing camera calibration parameters.

Single-Frame-Based Deep View Synchronization for Unsynchronized Multicamera Surveillance

We propose a synchronization model that operates in conjunction with existing DNN-based multi-view models to allow them to work on unsynchronized data.

Modeling Eye Movements by Integrating Deep Neural Networks and Hidden Markov Models

We model eye movements on faces through integrating deep neural networks and hidden Markov Models (DNN+HMM).

Crowd Counting in the Frequency Domain

We derive loss functions in the frequency domain for training density map regression for crowd counting.

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.

Recent Datasets and Code [more]

Modeling Eye Movements with Deep Neural Networks and Hidden Markov Models (DNN+HMM)

This is the toolbox for modeling eye movements and feature learning with deep neural networks and hidden Markov models (DNN+HMM).

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.

Teaching

  • CS 4487 – Machine Learning (undergraduate) — 2015A-2018A.
  • CS 5487 – Machine Learning: Principles & Practice (postgraduate) — 2012A-2022A.
  • CS 5489 – Machine Learning: Algorithms & Applications (postgraduate) — 2020B-2023B
  • 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)

Service

  • Action Editor, Transactions on Machine Learning Research (2022-now)
  • Guest Editor, Special Issue on “Applications of artificial intelligence, computer vision, physics and econometrics modelling methods in pedestrian traffic modelling and crowd safety”, Transportation Research Part C: Emerging Technologies (2022-23)
  • Senior Area Editor, IEEE Signal Processing Letters (2016-2020)
  • Associate Editor, IEEE Signal Processing Letters (2014-2016)
  • Conference Area Chair
    • CVPR – 2020, 2023
    • 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), 2020-22
  • 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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