Overview
Kaizhu Huang works on machine learning, neural information processing, and pattern recognition. Before joining DKU, he was a full professor at Xi’an Jiaotong-Liverpool University (XJTLU) and Associate Dean of research in School of Advanced Technology, XJTLU. Prof. Huang obtained his PhD degree from Chinese University of Hong Kong (CUHK), Master degree from Institute of Automation, Chinese Academy of Sciences, and Bachelor degree from Xi'an Jiaotong University. He worked at Fujitsu Research Centre, CUHK, University of Bristol, Chinese Academy of Sciences from 2004 to 2012. He was the recipient of the 2011 Asia Pacific Neural Network Society Young Researcher Award. He received the best paper or book awards for seven times. He has published 9 books and over 230 international research papers including 120+ journal papers (e.g. IEEE T-PAMI, IEEE T-NNLS, IEEE T-BME, IEEE T-Cybernetics, JMLR) and 110+ conference papers (e.g. NeurIPS, IJCAI, SIGIR, UAI, CIKM, ICDM, ICML, ECML, CVPR). He serves as associated editors/advisory board members in a number of international journals and book series. He was invited as a keynote speaker in more than 30 international conferences or workshops.
Current Appointments & Affiliations
Professor of Electrical and Computer Engineering at Duke Kunshan University
·
2022 - Present
DKU Faculty
Director of Data Science Research Center (DSRC) at Duke Kunshan University
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2023 - Present
DKU Faculty
Recent Publications
You look from old classes: Towards accurate few shot class-incremental learning
Journal Article Pattern Recognition · April 1, 2026 Few-shot class incremental learning (FSCIL) is a common but difficult task that faces two challenges: catastrophic forgetting of old classes and insufficient learning of new classes with limited samples. Recent wisdom focuses on preventing catastrophic for ... Full text CiteIDEA: Image description enhanced CLIP-adapter for image classification
Journal Article Pattern Recognition · March 1, 2026 CLIP (Contrastive Language-Image Pre-training) has attained great success in pattern recognition and computer vision. Transferring CLIP to downstream tasks (e.g., zero- or few-shot classification) is a hot topic in multimodal learning. However, current stu ... Full text CitePoint2pix-Zero: Point-driven refined diffusion for multi-object image editing
Journal Article Pattern Recognition · February 1, 2026 Semantic image editing methods employing large-scale diffusion models have made significant strides in precise and controlled image editing with text prompts as guidance. However, these models struggle to handle complex images containing hard-described obj ... Full text CiteEducation, Training & Certifications
Chinese University of Hong Kong (Hong Kong) ·
2004
Ph.D.