Dr. Antoni B. Chan
Associate 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 an associate 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]

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]

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.

Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over Simplex

We propose a generic framework to approximate the output probability distribution induced by a Bayesian NN model posterior with a parameterized model and in an amortized fashion. The aim is to approximate the predictive uncertainty of a specific Bayesian model, meanwhile alleviating the heavy workload of MC integration at testing time.

Compare and Reweight: Distinctive Image Captioning Using Similar Images Sets

To improve the distinctiveness of image captions, we first propose a metric, between-set CIDEr (CIDErBtw), to evaluate the distinctiveness of a caption with respect to those of similar images, and then propose several new training strategies for image captioning based on the new distinctiveness measure.

Recent Datasets and Code [more]

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.

CityStreet: Multi-view crowd counting dataset

Datasets for multi-view crowd counting in wide-area scenes. Includes our CityStreet dataset, as well as the counting and metadata for multi-view counting on PETS2009 and DukeMTMC.

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
    • 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
    • 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:

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

IEEE Copyright Notice
©IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.