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]
- Meta-Graph Adaptation for Visual Object Tracking.
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In: IEEE International Conference on Multimedia and Expo (ICME), to appear Jul 2021 (oral). - A Generalized Loss Function for Crowd Counting and Localization.
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In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), to appear Jun 2021. - Cross-View Cross-Scene Multi-View Crowd Counting.
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In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), to appear Jun 2021. - Progressive Unsupervised Learning for Visual Object Tracking.
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In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), to appear Jun 2021 (oral). - Bayesian Nested Neural Networks for Uncertainty Calibration and Adaptive Compression.
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In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), to appear Jun 2021. - Do portrait artists have enhanced face processing abilities? Evidence from hidden Markov modeling of eye movements.
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Cognition, 211(104616), June 2021. - Eye Movement analysis with Hidden Markov Models (EMHMM) with co-clustering.
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Behavior Research Methods, to appear 2021. - Applying the Hidden Markov Model to Analyze Urban Mobility Patterns: An Interdisciplinary Approach.
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Chinese Geographical Science, 31(1):1-13, Feb 2021. - Fine-Grained Crowd Counting.
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IEEE Trans. on Image Processing (TIP), 30:2114-2126, Jan 2021. [code | data] - PRIMAL-GMM: PaRametrIc MAnifold Learning of Gaussian Mixture Models.
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IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), to appear 2021. [code] - Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets.
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IEEE Trans. on Image Processing (TIP), 30:1439-1452, 2021. - Visual Tracking via Dynamic Memory Networks.
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IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 43(1):360-374, Jan 2021. [code] - Kernel-based Density Map Generation for Dense Object Counting.
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IEEE Trans. Pattern Analysis and Machine Intelligence (TPAMI), to appear 2020. - Modeling Noisy Annotations for Crowd Counting.
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In: Neural Information Processing Systems (NeurIPS), Dec 2020. [supplemental] - On Diversity in Image Captioning: Metrics and Methods.
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IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), to appear 2020.
Selected Publications [more]
- Incorporating Side Information by Adaptive Convolution.
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International Journal of Computer Vision (IJCV), 128:2897-2918, July 2020. - Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View Fusion CNNs.
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In: IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), Long Beach, June 2019. [dataset&code] - Density-Preserving Hierarchical EM Algorithm: Simplifying Gaussian Mixture Models for Approximate Inference.
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IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 41(6):1323-1337, June 2019. - Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks - Counting, Detection, and Tracking.
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IEEE Trans. on Circuits and Systems for Video Technology (TCSVT), 29(5):1408-1422, May 2019. - Eye Movement Patterns in Face Recognition are Associated with Cognitive Decline in Older Adults.
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Psychonomic Bulletin & Review, 25(6):2200-2207, Dec 2018. - Maximum-Margin Structured Learning with Deep Networks for 3D Human Pose Estimation.
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International Journal of Computer Vision (IJCV), 122(1):149-168, March 2017. - Counting People Crossing a Line using Integer Programming and Local Features.
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IEEE Trans. on Circuits and Systems for Video Technology (TCSVT), 26(10):1955-1969, Oct 2016. [appendix] - Understanding eye movements in face recognition using hidden Markov models.
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Journal of Vision, 14(11):8, Sep 2014. - Clustering hidden Markov models with variational HEM.
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Journal of Machine Learning Research (JMLR), 15(2):697-747, Feb 2014. [code] - Clustering Dynamic Textures with the Hierarchical EM Algorithm for Modeling Video.
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IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 35(7):1606-1621, Jul 2013. [appendix] - Counting People with Low-Level Features and Bayesian Regression.
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IEEE Trans. on Image Processing (TIP), 21(4):2170-2177, May 2012. - Generalized Gaussian Process Models.
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In: IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Colorado Springs, Jun 2011. [supplemental] - Layered dynamic textures.
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IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 31(10):1862-1879, Oct 2009. - Modeling, clustering, and segmenting video with mixtures of dynamic textures.
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IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 30(5):909-926, May 2008. - Modeling music as a dynamic texture.
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IEEE Trans. on Audio, Speech and Language Processing (TASLP), 18(3):602-612, Mar 2010. - Supervised learning of semantic classes for image annotation and retrieval.
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IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 29(3):394-410, Mar 2007.
Google Scholar
Microsoft Academic
orcid.org/0000-0002-2886-2513
Scopus ID: 14015159100
Recent Project Pages [more]
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.
- , "Fine-Grained Crowd Counting." IEEE Trans. on Image Processing (TIP), 30:2114-2126, Jan 2021. [code | data]
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.
- , "Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets." IEEE Trans. on Image Processing (TIP), 30:1439-1452, 2021.
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.
- , "Modeling Noisy Annotations for Crowd Counting." In: Neural Information Processing Systems (NeurIPS), Dec 2020. [supplemental]
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.
- , "Accelerating Monte Carlo Bayesian Prediction via Approximating Predictive Uncertainty over the Simplex." IEEE Transactions on Neural Networks and Learning Systems (TNNLS), to appear 2020.
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.
- , "Compare and Reweight: Distinctive Image Captioning Using Similar Images Sets." In: European Conference on Computer Vision (ECCV), Aug 2020 (oral).
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.
- Files: dataset (1.2GB), code
- Project Page
- If you use this dataset please cite:
Parametric Manifold Learning of Gaussian Mixture Models (PRIMAL-GMM) Toolbox
This is a python toolbox learning parametric manifolds of Gaussian mixture models (GMMs).
- Files: download here
- Project page
- If you use this toolbox please cite:
PRIMAL-GMM: PaRametrIc MAnifold Learning of Gaussian Mixture Models.
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IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), to appear 2021.
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.
- Files: download here
- Project page
- If you use this toolbox please cite:
Eye movement analysis with switching hidden Markov models.
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Behavior Research Methods, 52:1026-1043, June 2020.
EgoDaily – Egocentric dataset for Hand Disambiguation
Egocentric hand detection dataset with variability on people, activities and places, to simulate daily life situations.
- Files: download page
- If you use this dataset please cite:
Is that my hand? An egocentric dataset for hand disambiguation.
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Image and Vision Computing, 89:131-143, Sept 2019.
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.
- Files: download page
- Project page
- If you use this dataset please cite:
Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View Fusion CNNs.
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In: IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), Long Beach, June 2019.
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
- 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
- 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.
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