Prof. Antoni B. Chan

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.
  • Research Assistant and Technical Assistant positions are available for a project on using ChatGPT in teaching. Details here.

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]

  • Scalable Video Object Segmentation with Simplified Framework.
    Qiangqiang Wu, Tianyu Yang, Wei Wu, and Antoni B. Chan,
    In: International Conf. Computer Vision (ICCV), Paris, to appear 2023.
  • Modeling Noisy Annotations for Point-Wise Supervision.
    Jia Wan, Qiangqiang Wu, and Antoni B. Chan,
    IEEE Trans. Pattern Analysis and Machine Intelligence (TPAMI), to appear 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), 45(8):10519-10534, Aug 2023 (online Feb 2023).
  • Human Attention-Guided Explainable AI for Object Detection.
    Guoyang Liu, Jindi Zhang, Antoni B. Chan, and Janet H. Hsiao,
    In: Annual Conference of the Cognitive Science Society, July 2023.
  • TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization.
    Ziquan Liu, Yi Xu, Xiangyang Ji, and Antoni B. Chan,
    In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2023.
  • Optimal Transport Minimization: Crowd Localization on Density Maps for Semi-Supervised Counting.
    Wei Lin and Antoni B. Chan,
    In: IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), Jun 2023 (highlight).
  • DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking Tasks.
    Qiangqiang Wu, Tianyu Yang, Ziquan Liu, Baoyuan Wu, Ying Shan, and Antoni B. Chan,
    In: IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), Jun 2023. [code]
  • Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios.
    Xueying Zhan, Zeyu Dai, Qingzhong Wang, Qing Li, Haoyi Xiong, Dejing Dou, and Antoni B. Chan,
    Transactions on Machine Learning Research (TMLR), June 2023.
  • ODAM: Gradient-based Instance-Specific Visual Explanations for Object Detection.
    Chenyang Zhao and Antoni B. Chan,
    In: Intl. Conf. on Learning Representations (ICLR), Rwanda, May 2023. [code]
  • 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, May 2023.
  • A Lightweight and Detector-Free 3D Single Object Tracker on Point Clouds.
    Yan Xia, Qiangqiang Wu, Wei Li, Antoni B. Chan, and Uwe Stilla,
    IEEE Trans. on Intelligent Transportation Systems, 24(5):5543-5554, May 2023.
  • Visual attention to own- vs. other-race faces: Perspectives from learning mechanisms and task demands.
    Janet H. Hsiao and Antoni B. Chan,
    British Journal of Psychology, 114(S1):17-20, May 2023.
  • Jobs-housing balance and travel patterns among different occupations as revealed by Hidden Markov mixture models: the case of Hong Kong.
    Feiyang Zhang, Becky P.Y. Loo, Hui Lan, Antoni B. Chan, and Janet H. Hsiao,
    Transportation, April 2023.
  • A Comparative Survey of Deep Active Learning.
    Xueying Zhan, Qingzhong Wang, Kuan-hao Huang, Haoyi Xiong, Dejing Dou, and Antoni B. Chan,
    arXiv:2203.13450, Mar 2023.
  • Clustering Hidden Markov Models With Variational Bayesian Hierarchical EM.
    Hui Lan, Ziquan Liu, Janet H. Hsiao, Dan Yu, and Antoni B. Chan,
    IEEE Trans. on Neural Networks and Learning Systems (TNNLS), 34(3):1537-1551, March 2023 (online 2021).

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]

Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios

We propose a batch-mode Pareto Optimization Active Learning (POAL) framework for Active Learning under Out-of-Distribution data scenarios.

ODAM: Gradient-based Instance-specific Visual Explanation for Object Detection

We propose the gradient-weighted Object Detector Activation Maps (ODAM), a visualized explanation technique for interpreting the predictions of object detectors, including class score and bounding box coordinates.

A Comparative Survey of Deep Active Learning

We present a comprehensive comparative survey of 19 Deep Active Learning approaches for classification tasks.

A Comparative Survey: Benchmarking for Pool-based Active Learning

We introduce an active learning benchmark comprising 35 public datasets and experiment protocols, and evaluate 17 pool-based AL methods.

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.

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-2024B.
  • CS 5489 – Machine Learning: Algorithms & Applications (postgraduate) — 2020B-2023A
  • 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

  • Associate Editor, IEEE Transactions on Pattern Analysis and Machine Intelligence (2023-now)
  • 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, 2023
    • ICML – 2021, 2022, 2023
    • ICLR – 2021, 2023, 2024
    • 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, 2023
    • 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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