| Prof. Tao Lei Shaanxi University of Science & Technology, China Lei Tao is a professor and doctoral supervisor at Shaanxi University of Science and Technology. He is alos the vice dean of the School of Electronic Information and Artificial Intelligence, and Senior Member of IEEE/CCF/CSIG. He is selected from the Shaanxi Provincial High level Talent Program, Shaanxi Provincial Outstanding Youth, Stanford Top 2% Global Scientists List, etc. He is a deputy editor, editorial board member, guest editor, etc. for 7 journals, and serves as conference chairman, technical committee chairman, publicity chairman, reward committee chairman, branch chairman, etc. in more than 20 international conferences. His main research areas are computer vision, machine learning, etc. At present, He has published 4 collections of specialized/authored and conference papers, and have published over 100 papers in international journals and conferences such as IEEE TIP, IEEE TMI, IEEE TFS, IEEE TGRS, and IJCAI. Among them, 6 papers are ESI highly cited papers. His Google Academic Citation has exceeded 4400. He hosted many projects such as the National Natural Science Foundation of China (5 projects), Shaanxi Provincial Outstanding Youth Fund, and Shaanxi Provincial Key Research and Development Program. He won the second prize of Shaanxi Province Science and Technology Award and the first prize of Gansu Province Higher Education Research Excellent Achievement Award as the first complete person. Title：Remote Sensing Image Change Detection Using Lightweight Deep Network Models Abstract：Remote sensing image change detection utilizes multi temporal remote sensing data to extract change information from images, and then quantitatively analyzes and monitors the characteristics and processes of surface changes. Remote sensing image change detection can be widely applied in fields such as land use, urban planning, disaster assessment, and crop growth monitoring. Currently, remote sensing image change detection faces problems such as limited annotated data, a large mountain of model parameters, and low detection accuracy in complex scenes. To address these issues, we propose high-precision and lightweight change detection network models for change detection under complex scenes. The proposed network models not only achieve higher change detection accuracy than mainstream methods, but also has fewer parameters and computational complexity. |
 | Prof. Xiangjian He University of Nottingham Ningbo China, China Professor Xiangjian (Sean) He received his PhD in Computer Science from the University of Technology Sydney in 1999. He is currently the Deputy Head of Computer Science School and the Director of Computer Vision and Intelligent Perception Laboratory at the University of Nottingham Ningbo China (UNNC). He is a National Talent of China with a Chair Professor title, and in list of the 'World Top 2% Scientists' reported by Stanford University in 2022, 2023, etc. He was the Professor of Computer Science and the Leader of Computer Vision and Pattern Recognition Laboratory at the Global Big Data Technologies Centre (GBDTC) at the University of Technology Sydney (UTS) from 2011-2022. He was an IEEE Signal Processing Society Student Committee member. He was involved in a team receiving a UTS Chancellor's Award for Research Excellence through Collaboration in 2018. He has been awarded 'Internationally Registered Technology Specialist' by International Technology Institute (ITI). He led the UTS and Hong Kong Polytechnic University (PolyU) joint research project teams winning the 1st Runner-Up prize for the 2017 VIP Cup, and the champion for the 2019 VIP Cup, awarded by IEEE Signal Processing Society. In 2021, the team, PolyUTS, led by Prof Lam of PolyU and co-led by Prof He of UTS again won the 1st Runner-Up award for the 2021 VIP Cup. He has been carrying out research mainly in the areas of computer vision, data analytics and machine learning in the previous years. He has recently been leading his research teams for deep-learning-based research for human behavious recognition, human counting and density estimation, tiny object detection, biomedical applications, saliency detection, natural language processing, cybersecurity, face and face expression recognition, road sign detection, license plate recognition, etc. He has played various chair roles in many international conferences such as ACM MM, MMM, ICDAR, IEEE BigDataSE, IEEE BigDataService, IEEE TrustCom, IEEE CIT, IEEE AVSS, IEEE ICPR and IEEE ICARCV. |
