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professor at Universiti Malaya



Google Scholar | [Curriculum Vitae]
Follow @cs-chan

My research interests include computer vision and machine learning, where I lead a young and energetic research team that has published more than 100 papers in related top peer-review conferences and journals (e.g. CVPR, NeurIPS, TPAMI, TIP etc). I was the founding Chair for IEEE Computational Intelligence Society, Malaysia chapter.

Also currently, I serve as the Associate Editor of Pattern Recognition (Elsevier), and have co-organized several conferences/workshops/tutorials/challenges related to computer vision/machine learning. I was the recipient of Top Research Scientists Malaysia (TRSM) in 2022, Young Scientists Network Academy of Sciences Malaysia (YSN-ASM) in 2015 and Hitachi Research Fellowship in 2013. Besides that, I am also a senior member (IEEE), Professional Engineer (BEM) and Chartered Engineer (IET).

During 2020-2022, I was seconded to the Ministry of Science, Technology and Innovation (MOSTI) as the Lead of PICC Unit under COVID19 Immunisation Task Force (CITF), as well as the Undersecretary for Division of Data Strategic and Foresight.

Highlights:
        09/2022: One(1) paper to appear in AACL IJCNLP-2022.
        06/2022: Two(2) papers to appear in ICIP-2022.
        03/2022: One(1) paper to appear in ICPR-2022.

I am always interested to hear from prospective research students. Scholarships are available from time to time, contact me to enquire.


Latest Works

2

An Embarrassingly Simple Approach for Intellectual Property Rights Protection on Recurrent Neural Networks Star

This paper proposes a practical approach for the IPR protection on recurrent neural networks (RNN) with the Gatekeeper concept.

Z.Q. Tan, H.S. Wong and C.S. Chan
AACL IJCNLP 2022
[pdf (coming soon)] [code (coming soon)]

2

DeepIPR: Deep Neural Network Ownership Verification with Passports Star

We propose novel passport-based DNN ownership verification schemes which are both robust to network modifications and resilient to ambiguity attacks (i.e. the DNN model performance of an original task will be significantly deteriorated due to forged passports). Extension of NeurIPS 2019 (acceptance rate: 1428/6743 ~ 21.18%).

L. Fan, K.W. Ng, C.S. Chan and Q. Yang
IEEE Transactions on Pattern Analysis and Machine Intelligence (2022)
[pdf] [code]

2

CyEDA: Cycle-object Edge Consistency Domain Adaptation Star

This paper proposed CyEDA to perform global level domain adaptation (DA) without any pre-trained networks integration/annotation labels.

J.C. Beh, K.W. Ng, J.L. Kew, C-T, Lin, C.S. Chan, S-H. Lai and C. Zach
ICIP 2022
[pdf] [code]

2

ProX: A Reversed Once-for-All Network Training Paradigm for Efficient Edge Models Training in Medical Imaging

We propose a reversed OFA Network training algorithm - Progressive Expansion (ProX) that achieve up to 68% training time reduction.

S.W. Lim, C.S. Chan, E.R.M. Faizal and K.H. Ewe
ICIP 2022
[pdf]

2

Extremely Low-light Image Enhancement with Scene Text Restoration

This work deals with the problem of low-light image enhancement, in the context of scene texts.

P. Hsu, C-T. Lin, C.C. Ng, J-L. Kew, M.Y. Tan, S-H. Lai, C.S. Chan and C. Zach
ICPR 2022
[pdf]

2

One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective Star

This paper proposes a novel deep hashing model with only a single learning objective which is a simplification from most state of the art papers generally use lots of losses and regularizer.

J.T. Hoe, K.W. Ng, T. Zhang, C.S. Chan, Y-Z. Song and T. Xiang
NeurIPS 2021 (acceptance rate: 2372/9122 ~ 26.0%)
[pdf] [12mins Video] [code]

2

Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attacks Star

This paper presents a complete protection framework in both black-box and white-box settings to enforce IPR protection on GANs.

D.S. Ong, C.S. Chan, K.W. Ng, L. Fan and Q. Yang.
CVPR 2021 (acceptance rate: 1663/7015 ~ 23.7%)
[pdf] [10mins Video] [code]