Keynote Speakers

Qiguang Miao received the PhD degree from Xidian University in 2005. He is currently the professor and PhD degree supervisor with the School of Computer Science and Technology, Xidian University. He has authored or coauthored more than 100 papers in the significant international journals or conferences. His research interests include intelligent image/video understanding and Big Data.

苗启广，教授、博导，教育部高等学校教育技术与人工智能教学指导委员会委员、西安电子科技大学华山学者领军教授，西安电子科技大学信息化推进办公室主任、计算机科学与技术学院教授委员会主任、大数据与视觉智能重点实验室主任、协同智能系统教育部重点实验室副主任；教育部工程教育专业认证专家。连续三年入选斯坦福大学《全球前2%顶尖科学家榜单》(终身/年度影响力)；获中央网信办"网信优秀人才"、教育部新世纪优秀人才、山东省泰山学者产业创新领军人才、陕西省特支计划教学领军人才、陕西省教学名师等荣誉。在Cell子刊、IEEE汇刊、人工智能顶会等发表论文230篇，累计申请发明专利170余项，出版专著《智能教育技术及应用》等8部，获陕西省自然科学一等奖、国家级/省级教学成果奖等10项。

Speech Title: Big Data Analytics and Applications in Education and Teaching
Abstract: This report focuses on the current status and bottlenecks of multi-source educational data applications in the context of digital transformation in education. Centering on student campus life and learning data, as well as full-process teaching data, it introduces applications such as learning analytics, academic risk early warning, and teaching quality evaluation, and shares a case study of smart education platform construction. The report further discusses reflections on empowering personalized education and teaching with artificial intelligence and big data.

教育教学大数据分析与应用
报告聚焦教育数字化转型背景下，教育教学多源数据的应用现状与瓶颈。围绕学生大学校园学习生活中产生的数据、全流程教学过程中产生的海量数据，介绍基于大数据分析开展的学情分析、学业风险预警、教学质量评估等，并分享智慧教育教学平台建设案例。报告进一步探讨人工智能与大数据赋能个性化教育教学的思考。

Jihua Zhu received the Ph.D. degree in pattern recognition and intelligence systems from Xi'an Jiaotong University, Xi'an, China, in 2011. He is currently a Professor with the School of Software Engineering, Xi'an Jiaotong University, China. He is senior member of IEEE and Associate Editor of EURASIP Journal on Image and Video Processing. His research interests include computer vision and machine learning.

Speech Title: Self-Supervised Learning and Efficient Transfer for 3D–4D Point Cloud Understanding
Abstract: Leveraging large-scale unlabeled point clouds for generalizable representations and transferring static 3D knowledge to dynamic 4D scenes are key directions in 3D vision. Among self-supervised methods, masked point modeling is notable for its simplicity and strong transferability, yet faces three challenges: fixed masking fails to adapt to local geometry, deterministic reconstruction cannot capture multiple plausible structures of missing regions, and static-to-dynamic transfer suffers from modality gaps, few-shot overfitting, and high resource costs. This talk presents our recent progress on enhancing self-supervised point cloud representations and efficient transfer, covering representation complementarity, adaptive masking, probabilistic reconstruction, static–dynamic feature alignment, and lightweight spatiotemporal adaptation, toward unified 3D–4D point cloud understanding.

题目：面向3D—4D点云理解的自监督学习与高效迁移方法
利用大规模无标签点云学习通用表征，并将静态3D知识迁移至动态4D场景，是三维视觉领域的重要研究方向。现有自监督方法中，掩码点建模因结构简单、迁移性强而备受关注，但仍面临三方面挑战：固定掩码策略难以适配局部几何差异，确定性重建无法刻画缺失区域的多重可能结构，静态模型向动态数据迁移时存在模态差异、小样本过拟合及资源开销大等问题。本次报告将介绍我们在点云自监督表征增强与高效迁移方面的近期研究进展，重点围绕表征互补性构建、自适应掩码策略设计、概率化重建框架、静态—动态特征对齐，以及轻量化时空适配机制等若干方向展开讨论，旨在为3D与4D点云理解的统一建模提供有益的探索与参考。

 

Kaibing Zhang received the M.Sc. degree in computer software and theory from Xihua University, Chengdu, China, in 2005, and the Ph.D. degree in pattern recognition and intelligent system from Xidian University, Xi’an, China, in 2012. He is currently a Professor at the School of Computer Science, Xi’an Polytechnic University, Xi’an. His main research interests include pattern recognition, computer vision, and image super-resolution reconstruction. In these areas, he has published around 50 technical articles in refereed journals and proceedings, including IEEE Transactions on Image Processing, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Signal Processing Letters, Knowledge-Based Systems, Signal Processing (Elsevier), Neurocomputing, CVPR, and ICIP.

Speech Title: Research and Advances in Multimodal Knowledge-Driven Controllable Fashion Garment Image Generation
多模态知识驱动的时尚服装图像可控生成研究与进展
Abstract: In recent years, generative artificial intelligence (GenAI), represented by diffusion models and multimodal large language models, has advanced rapidly, driving image generation from content creation toward multimodal understanding, knowledge-enhanced generation, and fine-grained controllability. This evolution provides a new technological paradigm for the digital and intelligent transformation of fashion design. Fashion garment images involve complex structural, textural, and semantic attributes, while multimodal generation still faces significant challenges in semantic alignment, structure–texture coordination, local attribute control, and appearance consistency. This report first reviews the development and evolution of GenAI for image generation and analyzes the key challenges in multimodal controllable fashion garment image generation. It then presents our research on retrieval-augmented multi-granularity feature-aware generation and multimodal-conditioned fine-grained garment image generation, followed by recent studies on local multi-attribute editing and pose-constrained garment generation. Finally, the report discusses future directions, including multimodal knowledge enhancement, local multi-attribute editing, and physics-prior-driven garment generation, with the aim of advancing fashion image generation from visual content creation toward high-fidelity intelligent design that integrates semantic, structural, material, and physical knowledge. These developments are expected to provide new perspectives for leveraging AI in digital fashion and intelligent garment design.
摘要：近年来，以扩散模型和多模态大模型为代表的生成式人工智能快速发展，推动图像生成由内容创作向多模态理解、知识增强与精细化可控生成演进，为时尚服装数字化与智能化设计提供了新的技术范式。时尚服装图像具有复杂的结构、纹理与语义属性，多模态条件下的语义对齐、结构—纹理协同、局部属性控制及外观一致性仍面临诸多挑战。 本报告首先介绍生成式人工智能在图像生成领域的发展与演进，分析多模态时尚服装图像可控生成面临的关键问题；随后介绍本课题组围绕检索增强的多粒度特征感知生成和多模态条件驱动的细粒度服装图像生成开展的研究，进一步探讨局部多属性编辑与人体姿态约束下的服装生成方法。最后，报告将围绕多模态知识增强、局部多属性编辑以及物理先验驱动的服装生成等方向展望未来发展趋势，探索时尚服装图像生成由视觉内容创作向融合语义、结构、材质与物理规律的高保真智能设计演进，为人工智能赋能数字时尚与智能服装设计提供新的思路。

 

Invited Speakers

Prof. Dr. Mohan Lal Kolhe is a distinguished academic and researcher, currently serving as a Full Professor of Smart and Sustainable Electrical Energy Systems with Renewables and Hydrogen at the University of Agder in Norway. He is an internationally recognized expert in smart grids, renewable energy technologies, and hydrogen systems, with over three decades of global academic leadership and innovation.
Prof. Kolhe’s research spans integrated renewable energy systems, power electronics and grid stability, hydrogen production and utilization, and AI-enabled energy management. He has held significant academic positions at world-renowned institutions, including University College London (UK/Australia), University of Dundee (UK), University of Jyvaskyla (Finland), and the Hydrogen Research Institute (Canada). He also served on the Government of South Australia’s first Renewable Energy Board (2009–2011), contributing to progressive renewable energy policy development.
A prolific author and editor, Prof. Kolhe has published extensively and secured competitive funding from leading research bodies such as the Norwegian Research Council, EU, EPSRC, and BBSRC. His work has been ranked among the top 2% of scientists globally by Stanford University metrics, reflecting its high impact and influence in the transition toward sustainable energy systems.

Chao Fang received his B.S degree in Information Engineering from Wuhan University of Technology, Wuhan, China, in 2009, and the Ph.D. degree with the State Key Laboratory of Networking and Switching Technology in Information and Communication Engineering from Beijing University of Posts and Te4lecommunications, Beijing, China, in 2015. He joined the Beijing University of Technology in 2016 and now is an associate professor. From August 2013 to August 2014, he had been funded by China Scholarship Council to visit Carleton University, Ottawa, ON, Canada, as a joint doctorate. Moreover, he is the visiting scholars of University of Technology Sydney, Commonwealth Scientific and Industrial Research Organization, Hong Kong Polytechnic University, Kyoto University, Muroran Institute of Technology, and Queen Mary University of London. Dr. Fang is the senior member of IEEE, and the vice chair of technical affairs committee in IEEE ComSoc Asia/Pacific Region (2022-2023). Moreover, he served as the Technical Program Committee Chair of SPCNC 2024, the Session Chairs of ICC 2015, ICCC 2023, and WCNC 2024, Workshop Chairs of ICFEICT (2022-2024) and ICNCIC (2023-2024), and Poster Co-Chair of HotICN 2018. He won the Best Paper Award of IEEE ICFEICT 2022 and 2024, ICCSN 2024, and NCIC 2024. His current research interests include intelligent analysis and control, and intelligent cloud-edge-terminal cooperation computing.

Gang Yang, associate professor from Information Support Force Engineering University, serve a Committee Member of the Intelligent Evaluation Professional Committee at the Chinese Institute of Command and Control. He also works as a reviewer or PC member for prestigious conferences and journals, including TIP, ICASSP, and CogSci (all ranked CCF-A/B). His research portfolio includes leading 3 projects and contributing to 15 others, such as the National Key R&D Program of China. He has authored over 30 academic papers and holds 13 patents and software copyrights.

Chunlei Xia received his Ph.D. in the Department of Electronics and Electrical Engineering from Pusan National University in 2012. He then served as a Senior Researcher at the SPENALO Robot Center, PNU, before joining the Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences (CAS) in 2013. His research interests include marine monitoring technology, underwater optical imaging, intelligent ocean-observation instruments, and marine organism imaging and recognition systems. He has undertaken more than ten national-level research projects, including grants from the National Key R&D Program of China, the National Natural Science Foundation of China (NSFC), and talent programs of CAS. He has published over 40 papers, obtained 6 patents, and served as Guest Editor and reviewer for several SCI-indexed journals.

Dongshik Kang is a Professor in the Department of Computer Science & Intelligent Systems, Faculty of Engineering, at the University of the Ryukyus, Okinawa, Japan. He received his Ph.D. in Electrical and Information Science from Osaka Prefecture University, Japan, in 1999. He joined the University of the Ryukyus as an Assistant Professor in the Department of Information Engineering in April 1999 and has since contributed to education and research in intelligent information systems. Alongside his academic career, he has actively collaborated with industry and research institutions in Japan, promoting the practical application of intelligent technologies. His research focuses on machine learning, intelligent systems, biological data analysis, educational technology, and artificial intelligence, with particular interests in applying AI to education, and environmental data analysis. Professor Kang is a Fellow of the Institute of Electronics, Information and Communication Engineers (IEICE), a member of the Information Processing Society of Japan (IPSJ), and currently serves as Chair of the Okinawa Branch of the Institute of Internet, Broadcasting and Communication (IIBC) in Japan.