professor at Universiti Malaya

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.

      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.

Latest Works

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

Z.Q. Tan, H.S. Wong and C.S. Chan
AACL IJCNLP 2022 (oral long paper, acceptance rate: 87/554 ~ 15.7%)


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

pdf code

DeepIPR: Deep Neural Network Ownership Verification with Passports Star

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


We propose novel passport-based DNN ownership verification schemes which are both robust to network modifications and resilient to ambiguity attacks. Extension of NeurIPS2019 (acceptance rate: 1428/6743 ~ 21.18%).

pdf poster(NeurIPS2019) code

CyEDA: Cycle-object Edge Consistency Domain Adaptation Star

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


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

pdf slide poster 12mins Video code

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

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


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

pdf poster

Extremely Low-light Image Enhancement with Scene Text Restoration

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


This work deals with the problem of low-light image enhancement, in the context of scene texts. Particularly, a novel image enhancement framework is proposed to precisely restore the scene texts, as well as the overall quality of the image simultaneously under extremely low-light images conditions.


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

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%)


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 a large number (>4) of losses and regularizer. Further, with this learning objective, code balancing can be achieved by simply using a Batch Normalization (BN) layer and multi-label classification is also straightforward with label smoothing.

pdf 12mins Video code

Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attacks Star

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


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

pdf 10mins Video code