Prof. Jiwen Lu, Tsinghua University, China

computer vision, pattern recognition, embodied intelligence, and artificial intelligence safety

Jiwen Lu is a professor in the Department of Automation at Tsinghua University, the deputy director of a National Key Laboratory, and a Fellow of IEEE/IAPR. His primary research areas include computer vision, pattern recognition, embodied intelligence, and artificial intelligence safety. He has published over 150 papers in IEEE Transactions journals (including 42 in IEEE T-PAMI) and over 150 papers at CVPR/ICCV/ECCV. He holds over 60 authorized national invention patents and has led numerous major research projects, including two projects supported by the National Science Foundation for Young Scholars, three Key Programs of the National Science Foundation, one National Key R&D Program project, and two Beijing Municipal Key Projects. His accolades include the second prize of National Teaching Achievement Award, the first prize of the Ministry of Public Security Science and Technology Award, and two first prizes of the Natural Science Award from the Chinese Institute of Electronics. He serves as a council member of the China Simulation Federation and Director of its Visual Computing and Simulation Committee, vice chair of the Visual Cognition and Computing Committee of the Chinese Society of Image and Graphics, and vice chair of the Expert Advisory Committee of the Chinese Association of Automation. He is the Editor-in-Chief of Pattern Recognition Letters and an editorial board member for several IEEE journals, including T-IP, T-MM, T-CSVT, and T-BIOM. Under his mentorship, seven of his doctoral students have received outstanding doctoral dissertation awards from national academic societies and Beijing government.

Title: Embodied Intelligence Perception and Manipulation

Abstract: Embodied Intelligence is a hot and important research topic in the fields of artificial intelligence and unmanned systems, with significant application prospective in manufacturing, agriculture, and service industries. This talk will present recent major advancements in embodied intelligence perception and manipulation, covering methodologies and technologies such as online scene perception, unknown environment navigation, autonomous mobile manipulation, and lightweight model deployment. It will also explore applications in areas like modern services, industrial manufacturing, deep-sea exploration, and low-altitude security, concluding with an outlook on future development trends.

Prof. Guoyin Wang, Chongqing Normal University, China

Rough sets, granular computing, machine learning, knowledge technology, data mining, neural network, cognitive computing, etc.

Guoyin Wang received the B.S., M.S., and Ph.D. degrees from Xi’an Jiaotong University, Xian, China, in 1992, 1994, and 1996, respectively.  He worked at the University of North Texas, and the University of Regina, Canada, as a visiting scholar during 1998-1999.  He had worked at the Chongqing University of Posts and Telecommunications during 1996-2024, where he was a professor, the Vice-President of the University, the director of the Chongqing Key Laboratory of Computational Intelligence, the director of the Key Laboratory of Cyberspace Big Data Intelligent Security of the Ministry of Education, the director of Tourism Multi-source Data Perception and Decision Technology of the Ministry of Culture and Tourism,  and the director of the Sichuan-Chongqing Joint Key Laboratory of Digital Economy Intelligence and Security.  He was the director of the Institute of Electronic Information Technology, Chongqing Institute of Green and Intelligent Technology, CAS, China, 2011-2017.  He has been serving as the President of Chongqing Normal University since June 2024.  He is the author of over 20 books, the editor of dozens of proceedings of international and national conferences and has more than 400 reviewed research publications.  His research interests include rough sets, granular computing, machine learning, knowledge technology, data mining, neural network, cognitive computing, etc.  Dr. Wang was the President of International Rough Set Society (IRSS) 2014-2017, a Vice-President of the Chinese Association for Artificial Intelligence (CAAI) 2014-2025, and a council member of the China Computer Federation (CCF) 2008-2023.  He is currently a Supervisor of CAAI, and the President of Chongqing Association for Artificial Intelligence (CQAAI).  He is a Fellow of IRSS, AAIA, WIA, I2CICC, CAAI and CCF.  He is the recipient of the Technology Contribution Award in the Wu Wenjun Artificial Intelligence Science and Technology Award.  He has received over 10 awards for teaching and research achievements.

Title: Brain Cognition Inspired Artificial Intelligence

Abstract: Artificial intelligence (AI) has made breakthrough progress in surpassing some key human intelligence abilities such as visual intelligence, auditory intelligence, decision intelligence, and language intelligence in recent years. However, AI systems surpass certain human intelligence abilities in a statistical sense as a whole only. They are not true realization of these human intelligence abilities and behaviors. This talk reviews the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on Marr’s three-layer framework, explores and analyses the limitations of the current development of AI. Eight important future research directions and their scientific issues that need to be focused on in brain-inspired AI research are further discussed.

Prof. Yuming Fang, Jiangxi University of Finance and Economics, China

Artificial intelligence, intelligent processing of visual big data, virtual reality technology.

Yuming Fang is a professor with the department of Computer Science, Jiangxi University of Finance and Economics, Nanchang, China. He received the Ph.D. degree in Computer Engineering from Nanyang Technological University, Singapore, 2013. His research interests include multimedia processing, computer vision,etc. He is associate editor of IEEE Transaction on Multimedia. He is also a TPC Co-Chair for IEEE ICME 2023, IEEE ICIP 2027,etc.

Title: Computational Modeling for Visual Quality Assessment

Abstract:This talk will introduce the basic theories and methods for visual quality assessment. It will further introduce the computational models of quality assessment for smartphone photography and Virtual Reality images/videos. These works will cover subjective and objective quality assessment. Also, this talk will introduce the feature-driven and data-driven methods of visual quality assessment proposed by us in recent years

Prof. Lianghua He, Tongji University, China

Cognitive Computing, Artificial Intelligence and Pattern Recognition, Machine Learning, Image Processing

Lianghua He is a Professor at the School of Computer Science and Technology, Tongji University, and has been selected for the national high-level talent reward program. His research interests include medical image analysis and brain cognitive computing. He has led more than 20 projects, including key joint projects of the National Natural Science Foundation and key research and development programs of the Ministry of Science and Technology. He has published over 100 papers in total, including at conferences and journals such as CVPR, ICCV, IJCAI, AAAI, IEEE TNNLS, TIP, and TIFS. As the first contributor, he has received one First Prize of Science and Technology Progress Award of Shanghai and one Second Prize of Science and Technology Progress Award of the Ministry of Education. He has also participated in projects that won three provincial-level first prizes and two second prizes.

Title: Intelligent Analysis of Medical Images

Abstract: In the era of precision medicine, medical imaging, as a crucial basis for diagnosis and treatment, is becoming increasingly important. With the rapid advancement of intelligent technologies, intelligent analysis of medical images has emerged as a cutting-edge focus in the medical field. This report is based on the developmental trends of multimodal imaging acquisition, omics-based analysis, and intelligent modeling. It directly addresses industry challenges such as large deformations in images, scarcity of small-sample data, and poor model interpretability, and conducts an in-depth analysis of medical image analysis models.