Prof. Antoni B. Chan
Professor
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

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.
  • Currently there are two openings: 1) Postdoc; 2) Research Assistant

Recent Publications [more]

  • Dynamic Momentum Adaptation for Zero-Shot Cross-Domain Crowd Counting.
    Qiangqiang Wu, Jia Wan, and Antoni B. Chan,
    In: ACM Multimedia (MM), to appear Oct 2021.
  • Group-based Distinctive Image Captioning with Memory Attention.
    Jiuniu Wang, Wenjia Xu, Qingzhong Wang, and Antoni B. Chan,
    In: ACM Multimedia (MM), to appear Oct 2021 (oral).
  • A Comparative Survey: Benchmarking for Pool-based Active Learning.
    Xueying Zhan, Huan Liu, Qing Li, and Antoni B. Chan,
    In: International Joint Conf. on Artificial Intelligence (IJCAI), Survey Track, to appear Aug 2021.
  • Hierarchical Learning of Hidden Markov Models with Clustering Regularization.
    Hui Lan and Antoni B. Chan,
    In: 37th Conference on Uncertainty in Artificial Intelligence (UAI), Jul 2021.
  • Multiple-criteria Based Active Learning with Fixed-size Determinantal Point Processes.
    Xueying Zhan, Qing Li, and Antoni B. Chan,
    In: Subset Selection in Machine Learning: From Theory to Applications, ICML Workshop, July 2021.
  • Improve Generalization and Robustness of Neural Networks via Weight Scale Shifting Invariant Regularizations.
    Ziquan Liu, Yufei Cui, and Antoni B. Chan,
    In: ICML workshop on Adversarial Machine Learning, July 2021.
  • Meta-Graph Adaptation for Visual Object Tracking.
    Qiangqiang Wu and Antoni B. Chan,
    In: IEEE International Conference on Multimedia and Expo (ICME), to appear Jul 2021 (oral).
  • A Generalized Loss Function for Crowd Counting and Localization.
    Jia Wan, Ziquan Liu, and Antoni B. Chan,
    In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2021.
  • Cross-View Cross-Scene Multi-View Crowd Counting.
    Qi Zhang, Wei Lin, and Antoni B. Chan,
    In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR):557-567, Jun 2021. [dataset]
  • Progressive Unsupervised Learning for Visual Object Tracking.
    Qiangqiang Wu, Jia Wan, and Antoni B. Chan,
    In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2021 (oral).
  • Bayesian Nested Neural Networks for Uncertainty Calibration and Adaptive Compression.
    Yufei Cui, Ziquan Liu, Qiao Li, Antoni B. Chan, and Chun Jason Xue,
    In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2021.
  • Understanding the collinear masking effect in visual search through eye tracking.
    Janet H. Hsiao, Antoni B. Chan, Jeehye An, Su-Ling Yeh, and Jingling Li,
    Psychonomic Bulliten & Review, June 2021.
  • Do portrait artists have enhanced face processing abilities? Evidence from hidden Markov modeling of eye movements.
    Janet H. Hsiao, Jeehye An, Yueyuan Zheng, and Antoni B. Chan,
    Cognition, 211(104616), June 2021.
  • Hand disambiguation using attention neural networks in the egocentric perspective.
    Sergio R. Cruz and Antoni B. Chan,
    In: International Conference on Digital Image Processing (ICDIP), May 2021.
  • Eye Movement analysis with Hidden Markov Models (EMHMM) with co-clustering.
    Janet H. Hsiao, Hui Lan, Yueyuan Zheng, and Antoni B. Chan,
    Behavior Research Methods, April 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]

A Generalized Loss Function for Crowd Counting and Localization

We propose a generalized loss function for density map regression based on unbalanced optimal transport. We prove that pixel-wise L2 loss and Bayesian loss are special cases and sub-optimal solutions to our proposed loss. Since the predicted density will be pushed toward annotation positions, the density map prediction will be sparse and can naturally be used for localization.

Cross-View Cross-Scene Multi-View Crowd Counting

In this paper, we propose a cross-view cross-scene (CVCS) multi-view crowd counting paradigm, where the training and testing occur on different scenes with arbitrary camera layouts.

Fine-Grained Crowd Counting

In this paper, we propose 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. To enable research in this area, we construct a new dataset of four real-world fine-grained counting tasks: traveling direction on a sidewalk, standing or sitting, waiting in line or not, and exhibiting violent behavior or not.

Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets

We propose a new multiple-object tracking (MOT) paradigm, tracking-by-counting, tailored for crowded scenes. Using crowd density maps, we jointly model detection, counting, and tracking of multiple targets as a network flow program, which simultaneously finds the global optimal detections and
trajectories of multiple targets over the whole video.

Modeling Noisy Annotations for Crowd Counting

We model the annotation noise using a random variable with Gaussian distribution and derive the pdf of the crowd density value for each spatial location in the image. We then approximate the joint distribution of the density values (i.e., the distribution of density maps) with a full covariance multivariate Gaussian density, and derive a low-rank approximate for tractable implementation.

Recent Datasets and Code [more]

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.

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.

Parametric Manifold Learning of Gaussian Mixture Models (PRIMAL-GMM) Toolbox

This is a python toolbox learning parametric manifolds of Gaussian mixture models (GMMs).

Eye Movement analysis with Switching HMMs (EMSHMM) Toolbox

This is a MATLAB toolbox for analyzing eye movement data using switching hidden Markov models (SHMMs), for analyzing eye movement data in cognitive tasks involving cognitive state changes. It includes code for learning SHMMs for individuals, as well as analyzing the results.

EgoDaily – Egocentric dataset for Hand Disambiguation

Egocentric hand detection dataset with variability on people, activities and places, to simulate daily life situations.

Teaching

  • CS 4487 – Machine Learning (undergraduate) — 2015A-2018A.
  • CS 5487 – Machine Learning: Principles & Practice (postgraduate) — 2012A-2020A.
  • CS 5489 – Machine Learning: Algorithms & Applications (postgraduate) — 2020B-2021B.
  • 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.
  • Final Year Project Coordinator
  • Research Mentoring Scheme Coordinator
  • MSCS Project and Guided Study Coordinator
  • Multimedia Subject Group leader
  • BScCM Deputy Programme Leader

Service

  • 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
    • NeurIPS – 2020, 2021
    • ICML – 2021
    • ICLR – 2021
    • ICPR – 2020
    • Pacific Graphics – 2018
  • Conference Senior PC
    • AAAI – 2021, 2022
    • IJCAI – 2019-20
  • Conference Program Committees
    • CVPR – 2012-2019, 2021
    • 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

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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