SPEAKER I IN IPMV 2025

IEEE Fellow

Prof. James Tin-Yau Kwok, The Hong Kong University of Science and Technology, Hong Kong, China

 

BIO: Prof. Kwok is a Professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology. Prof. Kwok served / is serving as an Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems, Neurocomputing, Artificial Intelligence Journal, International Journal of Data Science and Analytics, and Editorial Board Member of Machine Learning. He is also serving as Senior Area Chairs of major machine learning / AI conferences including NeurIPS, ICML, ICLR, and IJCAI. He is recognized as the Most Influential Scholar Award Honorable Mention for "outstanding and vibrant contributions to the field of AAAI/IJCAI between 2009 and 2019". He is an IEEE Fellow, and will be the IJCAI-2025 Program Chair.

 

SPEAKER II IN IPMV 2025

 

Fellow of IEEE

Prof. Junsong Yuan, State University of New York at Buffalo, USA

 

BIO: Dr. Junsong Yuan is Professor and Director of Visual Computing Lab at Department of Computer Science and Engineering, State University of New York (SUNY) at Buffalo, USA. Before joining SUNY Buffalo, he was Associate Professor at Nanyang Technological University (NTU), Singapore. He obtained his Ph.D. from Northwestern University, M.Eng. from National University of Singapore, and B.Eng. from Huazhong University of Science Technology. He is a recipient of SONY Faculty Innovation Award (2024), SUNY Chancellor's Award for Excellence in Scholarship and Creative (2022), IEEE Trans. on Multimedia Best Paper (2016), Northwestern Outstanding EECS Ph.D. Thesis (2010), and Nanyang Assistant Professorship (2009). He serves as Editor-in-Chief of Journal of Visual Communication and Image Representation (JVCI), Associate Editor of IEEE Trans. on Pattern Analysis and Machine Intelligence (T-PAMI) and IEEE Trans. on Image Processing (T-IP). He also serves as General/Program Co-chair of ICME and Area Chair for CVPR, ICCV, ECCV, NeurIPS, ACM MM, etc. He is a Fellow of IEEE (2021) and IAPR (2018).

Speech Title: Intelligent Hand Sensing and Augmented Interaction

Abstract: Humans are the most intelligent beings on the planet not only because of our powerful brain but also due to the unique structure of our hands. Hands have been crucial tools for us to interact and change both the physical world and the virtual world such as metaverse. In this talk, we will discuss real-time hand sensing using optical cameras, and how it can enhance our interactions with physical world and metaverse. Towards 3D hand sensing from single 2D images, we will discuss how to leverage synthetic hand data to address high-dimensional regression problem of articulated hand pose estimation and 3D hand shape reconstruction. To improve the generalization ability of handling hands of various shapes and poses, we will also discuss invariant hand representation through disentanglement. The resulting systems can facilitate intelligent interactions in virtual and real environments using bare hands, as well as via hand object interactions.

 

 

SPEAKER III IN IPMV 2025

 

Fellow of IEEE, IAPR, and AAIA
Prof. Zhongfei (Mark) Zhang, Binghamton University State University of New York, USA

 

BIO: Zhongfei (Mark) Zhang is a professor at Computer Science Department, Binghamton University, State University of New York (SUNY), USA. He received a B.S. in Electronics Engineering (with Honors), an M.S. in Information Sciences, both from Zhejiang University, China, and a PhD in Computer Science from the University of Massachusetts at Amherst, USA. His research interests are in the broad areas of machine learning, data mining, computer vision, and pattern recognition, and specifically focus on multimedia/multimodal data understanding and mining. He was on the faculty of Computer Science and Engineering at the University at Buffalo, SUNY, before he joined the faculty of Computer Science at Binghamton University, SUNY. He is the author or co-author of the very first monograph on multimedia data mining and the very first monograph on relational data clustering. He has published over 200 papers in the premier venues in his areas. He holds more than thirty inventions, has served as members of the organization committees of several premier international conferences in his areas including general co-chair and lead program chair, and as editorial board members for several international journals. He served as a French CNRS Chair Professor of Computer Science at the University of Lille 1 in France, a JSPS Fellow in Chuo University, Japan, a QiuShi Chair Professor in Zhejiang University, China, as well as visiting professorships at many universities and research labs in the world when he was on leave from Binghamton University years ago. He received many honors including SUNY Chancellor’s Award for Scholarship and Creative Activities, SUNY Chancellor’s Promising Inventor Award, and best paper awards from several premier conferences in his areas. He is a Fellow of IEEE, IAPR, and AAIA.

 

Speech Title: On Deep Learning Reliability

 

SPEAKER IV IN IPMV 2025

 

Prof. Chi Man Pun, University of Macau, Macau, China

 

BIO: Prof. Pun received his Ph.D. degree in Computer Science and Engineering from the Chinese University of Hong Kong in 2002, and his M.Sc. and B.Sc. degrees from the University of Macau. He had served as the Head of the Department of Computer and Information Science, University of Macau from 2014 to 2019, where he is currently a Professor and in charge of the Image Processing and Pattern Recognition Laboratory. He has investigated many externally funded research Projects as PI, and has authored/co-authored more than 200 refereed papers in many top-tier Journals (including T-PAMI, T-IFS, T-IP, T-DSC, T-KDE, and T-MM) and Conferences (including CVPR, ICCV, ECCV, AAAI, ICDE, IJCAI, MM, and VR). He has also co-invented several China/US Patents, and is the recipient of the Macao Science and Technology Award 2014 and the Best Paper Award in the 6th Chinese Conference on Pattern Recognition and Computer Vision (PRCV2023). Dr. Pun has served as the General Chair for the 10th &11th International Conference Computer Graphics, Imaging and Visualization (CGIV2013, CGIV2014), the 13th IEEE International Conference on e-Business Engineering (ICEBE2016), and the General Co-Chair for the IEEE International Conference on Visual Communications and Image Processing (VCIP2020) and the International Workshop on Advanced Image Technology (IWAIT2022), and the Program/Local Chair for several other international conferences. He has also served as the SPC/PC member for many top CS conferences such as AAAI, CVPR, ICCV, ECCV, MM, etc. He is currently serving as the editorial board member for the journal of Artificial Intelligence (AIJ). Besides, he has been listed in the World's Top 2% Scientists by Stanford University since 2020. His research interests include Image Processing and Pattern Recognition; Multimedia Information Security, Forensic and Privacy; Adversarial Machine Learning and AI Security, etc. He is also a senior member of the IEEE.

Speech Title: Image Manipulation Localization with Deep Neural Networks

Abstract: Creating fake pictures has become more accessible than ever, but tampered images are more harmful because the Internet propagates misleading information so rapidly. Reliable digital forensic tools are, therefore, strongly needed. Traditional methods based on hand-crafted features are only useful when tampered images meet specific requirements, and the low detection accuracy prevents them from being used in realistic scenes. Recently proposed learning-based methods have improved accuracy, but neural networks usually require training on large labeled databases. This is because commonly used deep and narrow neural networks extract high-level visual features and neglect low-level features where there are abundant forensic cues. In this talk, we will discuss some solutions to this problem. Two novel image splicing localization methods are proposed using deep neural networks, which mainly concentrate on learning low-level forensic features and consequently can detect splicing forgery, although the network is trained on a small automatically generated splicing dataset.