| Zhihui Zhan (IEEE Fellow)Wuhan University, China BIO: Dr. Zhi-Hui Zhan received the Bachelor’s degree and the Ph. D. degree in Computer Science from Sun Yat-Sen University (SYSU), Guangzhou, P. R. China in 2007 and in 2013, respectively. He was a Lecture with the School of Information Science and Techonlogy, SYSU, from 2013-2014, was an Associate Professor with the School of Advanced Computing, SYSU, from 2014-2015, since 2016 is a Professor with the School of Computer Science and Engineering, South China University of Technology (SCUT), and since 2024 is a Professor with the College of Artificial Intelligence, Nankai University (NKU). His current research interests include artifical intelligence, computational intelligence, evolutionary computation, and swarm intelligence, especially the particle swarm optimization, genetic algorithm, ant colony optimization, differential evolution, and other optimization algorithms and their applications. He has published over 260 papers in international journals and conference proceedings, including more than 90 IEEE Transactions papers, and more than 20 of them are the Essential Science Indicator (ESI) Hot Paper and Highly Cited Paper. The “Adaptive Particle Swarm Optimization (APSO)”, the “Orthogonal Learning Particle Swarm Optimization (OLPSO)”, the “Multiple Populations for Multiple Objectives (MPMO)”, and some others are all listed as the Research Fronts of ESI. His published papers have been cited over 17000 times by the scholars all over the world, according to Google Scholar or Google Scholar (for Chinese), and over 10000 times in Web of Science. Prof. Zhan is an IEEE Fellow. and an AAIA Fellow. Speech Title：TBD Abstract：TBD |
| Xiao WuSouthwest Jiaotong University, China BIO: Xiao Wu is a Professor of Southwest Jiaotong University, Chengdu, China. He received the Ph.D. degree in Computer Science from City University of Hong Kong, Hong Kong. He was with the School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA, and at the School of Information and Computer Science, the University of California, Irvine, CA, USA, as a Visiting Scholar from 2006 to 2007 and from 2015 to 2016, respectively. His research interests include artificial intelligence, computer vision, and intelligent transportation systems. He has authored or co-authored more than 100 research papers in well-respected journals, such as TIP, TMM, and TMI, and prestigious proceedings like CVPR, ICCV, and ACM MM. He received the Second Prize of Natural Science Award of the Ministry of Education, China, in 2016, the Second Prize of Science and Technology Progress Award of Henan Province, China, in 2017, the Best Paper Award of International Conference on Multimedia Modeling, in 2021, and the Best Paper Award of ACM International Conference on Multimedia Asia in 2024. Speech Title：Human-Centered Interaction Detection and Applications Abstract：Understanding human-centered interactions is pivotal for developing intelligent systems capable of perceiving, interpreting, and responding to human activities in complex environments. This presentation will provide a comprehensive overview of advanced research in human-object interaction (HOI) detection, extending the scope to encompass critical interaction paradigms such as human-object-human (HOH), human-object-object (HOO) and contactless human-object interactions. The core focus will be on elucidating the methodologies, challenges, and practical applications of these interaction detection technologies within industrial settings. |
| Yong LuoWuhan University, China BIO: Yong Luo received the B.E. degree in Computer Science from the Northwestern Polytechnical University, Xi’an, China, and the D.Sc. degree in the School of Electronics Engineering and Computer Science, Peking University, Beijing, China. He was a Research Fellow with the School of Computer Science and Engineering, Nanyang Technological University, and is currently a Professor with the School of Computer Science, Wuhan University, China. His research interests are primarily on machine learning and data mining with applications to visual information understanding and analysis. He has authored or co-authored over 100 papers in top journals and prestigious conferences including Nature Machine Intelligence, Nature Communications, IEEE T-PAMI and IJCV. He is serving on editorial board for IEEE T-MM. He received the IEEE Globecom 2016 Best Paper Award, and was nominated as the IJCAI 2017 Distinguished Best Paper Award. He is also a co-recipient of the IEEE TMM 2023, IEEE ICME 2019 and IEEE VCIP 2019 Best Paper Awards. Speech Title：Deep Model Fusion Abstract：The paradigm of deep learning has significantly evolved in recent years, moving beyond traditional supervised learning to incorporate knowledge transfer and model editing. While these emerging techniques show promise in enhancing performance, accelerated training, and reducing labeled data dependency, their full potential and scalability to large foundation models remain unexplored. This talk provides an investigation of knowledge transfer and model fusion techniques for deep neural networks, covering (1) their background, motivation, and existing approaches; (2) a taxonomy for categorizing these techniques and the formal definitions for each category; (3) our recent contributions to the field, including adaptive model ensemble, plug-and-play techniques for improved model merging, adaptive weight-based model mixing, and model merging in Pareto optimization contexts; (4) discussion of the strengths, challenges, and future directions of model fusion techniques, providing a comprehensive overview of this rapidly evolving area in deep learning. |
| Prof. Xinguo YuCentral China Normal University, China BIO: Prof. Yu Xinguo is Professor at the National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China. He also holds an adjunct professorship at the University of Wollongong, Australia. He is the Chair of the Hubei Society of Artificial Intelligence in Research and Education. Prof. Yu's research primarily focuses on HI-AI collaboration, intelligent education, intelligent research, educational robotics, multimedia analysis, computer vision, and machine learning. With over 200 published research papers including over 30 SCI papers. Prof. Yu serves as an Associate Editor and Guest Editor for several international journals and has contributed significantly to the global academic community by serving as General Chair, Keynote Speaker, and Program Chair for more than 30 international conferences. Since 2021, he has pioneered and led the annual International Conference on Intelligent Education and Intelligent Research. Speech Title：AI and the Transformation of Work Paradigms Abstract：Artificial Intelligence (AI), particularly large language models (LLMs), is reshaping work paradigms across a wide range of domains that involve human intelligence. First, AI has transformed traditional research into a collaborative effort between humans and machines. Remarkably, AI-assisted research has even contributed to Nobel Prize-winning work. Second, AI technologies are redefining education by acting as alternative forms of instruction, influencing not only how students learn but also how they think and interact. Third, AI is revolutionizing work in numerous other sectors, including public service and home-based roles, by introducing new modes of operation and enhancing productivity. This talk explores these paradigm shifts and examines the implications of AI's pervasive role in the modern workforce. |
| Liang Chen (The University of Northern British)Columbia (UNBC), Canada BIO: Dr. Liang Chen is a Professor of Computer Science at the University of Northern British Columbia (UNBC) in British Columbia, Canada, where he has served since 2001 and led the department as chair (2005–2009) and acting chair (2021–2022). He earned a BSc in Computer Software from Huazhong University of Science and Technology (1988) and a PhD in Computer Science from the Institute of Software, Chinese Academy of Sciences (1994). Before joining UNBC, he worked in academia and industry in China, Japan, and France. His research spans pattern recognition, image processing, computational geometry, intelligent language tutoring systems, data mining, bioinformatics, computational intelligence (including fuzzy systems and neural networks), and voting schemes. He was the founding chair of the IEEE Northern British Columbia Subsection, and his biography appears in the 20th and 21st editions of Marquis Who’s Who in the World. Speech Title: Seeing Like a Machine: Why Stability Beats Accuracy in Subjective Recognition Abstract: In domains such as human or animal face recognition—and even leadership selection—clear, universally accepted standards for “correct” outcomes are often absent. These are subjective pattern-recognition problems. Computing machines excel at tasks requiring massive computation, yet when rules are underspecified, performance targets become misaligned with problem knowledge. This talk examines the expectation–knowledge gap and argues that pursuing high accuracy on arbitrary or shifting labels reduces research to trial and error. Instead, evaluation should prioritize stability—the consistency of outputs under perturbations to data, labels, model settings, and noise. Centering stability provides a more meaningful basis for progress on subjective tasks and better aligns algorithm design with real-world uncertainty. |
