Prof. Antoni B. Chan
BSc MEng Cornell, PhD UC San Diego
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
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.
- My collaborator has a postdoc position available on Explainable AI (XAI).
Recent Publications [more]
- Dynamic Momentum Adaptation for Zero-Shot Cross-Domain Crowd Counting.
In: ACM Multimedia (MM), Oct 2021.
- Group-based Distinctive Image Captioning with Memory Attention.
In: ACM Multimedia (MM), Oct 2021 (oral). [supplemental]
- BEV-Net: Assessing Social Distancing Compliance by Joint People Localization and Geometric Reasoning.
In: Intl. Conference on Computer Vision (ICCV), Oct 2021.
- A Comparative Survey: Benchmarking for Pool-based Active Learning.
In: International Joint Conf. on Artificial Intelligence (IJCAI), Survey Track, Aug 2021.
- Clustering Hidden Markov Models With Variational Bayesian Hierarchical EM.
IEEE Trans. on Neural Networks and Learning Systems (TNNLS), To appear 2021.
- The effects of attentional and interpretation biases on later pain outcomes among younger and older adults: A prospective study.
European Journal of Pain, [online] Aug 2021.
- Hierarchical Learning of Hidden Markov Models with Clustering Regularization.
In: 37th Conference on Uncertainty in Artificial Intelligence (UAI), Jul 2021.
- Multiple-criteria Based Active Learning with Fixed-size Determinantal Point Processes.
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.
In: ICML workshop on Adversarial Machine Learning, July 2021.
- Meta-Graph Adaptation for Visual Object Tracking.
In: IEEE International Conference on Multimedia and Expo (ICME), Jul 2021 (oral).
- System and Method for Counting Objects.
US Patent US11048948B2, June 2021.
- A Generalized Loss Function for Crowd Counting and Localization.
In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2021. [supplemental]
- Progressive Unsupervised Learning for Visual Object Tracking.
In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2021 (oral). [supplemental]
- Bayesian Nested Neural Networks for Uncertainty Calibration and Adaptive Compression.
In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2021.
Selected Publications [more]
- On Diversity in Image Captioning: Metrics and Methods.
IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), to appear 2020.
- Incorporating Side Information by Adaptive Convolution.
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.
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.
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.
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.
Psychonomic Bulletin & Review, 25(6):2200-2207, Dec 2018.
- Maximum-Margin Structured Learning with Deep Networks for 3D Human Pose Estimation.
International Journal of Computer Vision (IJCV), 122(1):149-168, March 2017.
- Counting People Crossing a Line using Integer Programming and Local Features.
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.
Journal of Vision, 14(11):8, Sep 2014.
- Clustering hidden Markov models with variational HEM.
Journal of Machine Learning Research (JMLR), 15(2):697-747, Feb 2014. [code]
- Clustering Dynamic Textures with the Hierarchical EM Algorithm for Modeling Video.
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.
IEEE Trans. on Image Processing (TIP), 21(4):2170-2177, May 2012.
- Layered dynamic textures.
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.
IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 30(5):909-926, May 2008.
- Modeling music as a dynamic texture.
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.
IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 29(3):394-410, Mar 2007.
Recent Project Pages [more]
We analyze eye movement data on stimuli with different feature layouts. Through co-clustering HMMs, we discover common strategies on each stimuli and cluster subjects with similar strategies.
- "Eye Movement analysis with Hidden Markov Models (EMHMM) with co-clustering." Behavior Research Methods, April 2021.,
In this paper, we propose a novel meta-graph adaptation network (MGA-Net) to effectively adapt backbone feature extractors in existing deep trackers to a specific online tracking task.
- "Meta-Graph Adaptation for Visual Object Tracking." In: IEEE International Conference on Multimedia and Expo (ICME), Jul 2021 (oral).,
In this paper, we propose a progressive unsupervised learning (PUL) framework, which entirely removes the need for annotated training videos in visual tracking.
- "Progressive Unsupervised Learning for Visual Object Tracking." In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2021 (oral). [supplemental],
We propose a fully nested neural network (FN3) that runs only once to build a nested set of compressed/quantized models, which is optimal for different resource constraints. We then propose a Bayesian version that estimates the ordered dropout hyperparameter and has well calibrated uncertainty.
- "Fully Nested Neural Network for Adaptive Compression and Quantization." In: International Joint Conf. on Artificial Intelligence (IJCAI), Yokohama, July 2020. [supplemental],
- "Bayesian Nested Neural Networks for Uncertainty Calibration and Adaptive Compression." In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2021.,
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.
- "A Generalized Loss Function for Crowd Counting and Localization." In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2021. [supplemental],
Recent Datasets and Code [more]
CVCS: Cross-View Cross-Scene Multi-View Crowd Counting Dataset
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).
- Files: download here
- Project page
- If you use this toolbox please cite:
PRIMAL-GMM: PaRametrIc MAnifold Learning of Gaussian Mixture Models.
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.
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.
Image and Vision Computing, 89:131-143, Sept 2019.
- 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
- 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, 2022
- 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.
Prof. Antoni Chan,
Department of Computer Science,
City University of Hong Kong,
Tat Chee Avenue,
Kowloon Tong, Hong Kong.
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