Keynote Speakers

Junyu Dong

Professor, Ocean University of China

Abstract

Owing to the complex and harsh conditions of underwater operating environments, traditional underwater photography cannot realize precise three-dimensional measurements, whereas acoustic imaging fails to acquire high-resolution data as well as surface color information. High-precision underwater optical 3D imaging boasts extensive application prospects, ranging from the inspection and monitoring of marine engineering and other underwater infrastructure, to high-fidelity 3D scanning and measurement of seabed structures such as coral reefs, oyster beds and shell ridges, and further to high-precision 3D mapping of underwater cables.

This report mainly introduces the high-precision 3D imaging equipment and technology developed by our research team. The system achieves millimeter-level measurement accuracy and excels in multiple key performance indicators, including range resolution, lateral resolution and sampling rate. Furthermore, it can be flexibly deployed on various types of underwater vehicles.

Biography

Prof. Junyu Dong received his Ph.D. in November 2003 from Heriot-Watt University, UK. He is a corresponding member of the National Academy of Artificial Intelligence (NAAI), and currently a professor and the Dean of the Faculty of Information Science and Engineering at Ocean University of China. In 2020, he was awarded a Leverhulme Trust Visiting Professorship hosted by the University of Portsmouth.

His research interests include 3D underwater imaging and machine learning with applications in marine science. He has been the principal investigator of more than 10 research projects supported by the Natural Science Foundation of China and the Ministry of Science and Technology. He has published more than 200 major journal and conference papers. Professor Junyu Dong has developed a series of high-resolution underwater 3D imaging systems, which have been deployed to obtain 3D data of seabed topography, corals, underwater power cables, port seabed foundations, and various other underwater structures. He is the founding editor of the Journal of Intelligent Marine Technology and Systems, and also a Chairman of the Qingdao Chapter of the Association for Computing Machinery (ACM).

Celimuge Wu

Professor, The University of Electro-Communications, Japan

Abstract

This talk presents a novel low-latency semantic video communication framework tailored for remote driving scenarios. Unlike conventional video transmission approaches, the proposed system adopts an asymmetric encoder–decoder architecture that significantly reduces transmission overhead by conveying only a compact set of semantic features rather than raw video data.

At the receiver, high-quality video is reconstructed using advanced generative AI techniques, enabling both low latency and high visual fidelity. To validate the proposed framework, we design and implement a prototype system that seamlessly integrates semantic feature extraction, efficient transmission, and deep learning–based video reconstruction.

Experimental results demonstrate the effectiveness of the proposed approach in achieving ultra-low latency while maintaining high visual quality, highlighting its strong potential for next-generation intelligent transportation systems.

Biography

Celimuge Wu is a Professor and Director of the Meta-Networking Research Center at The University of Electro-Communications, Japan. His research interests include semantic communications, vehicular networks, edge computing, the Internet of Things (IoT), and AI-driven wireless networking and computing.

He serves as an Associate Editor for IEEE Transactions on Networking, IEEE Transactions on Cognitive Communications and Networking, IEEE Transactions on Network Science and Engineering, and IEEE Transactions on Green Communications and Networking. He is the Vice Chair (Asia Pacific) of the IEEE Technical Committee on Big Data. Professor Wu is a recipient of the 2021 IEEE Communications Society Outstanding Paper Award, the 2021 IEEE Internet of Things Journal Best Paper Award, the 2020 IEEE Computer Society Best Paper Award, and the 2019 IEEE Computer Society Best Paper Award Runner-Up. He is a Distinguished Lecturer of the IEEE Vehicular Technology Society. He is a Foreign Fellow of the Engineering Academy of Japan and a Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA).

Zhu Li

Professor, University of Missouri, Kansas City

Abstract

Remote sensing and vision problems such as object detection and recognition from various active and passive sensors are of great value to many precision agriculture, defense, and disaster relief use cases. Usually, due to sensor or communication link limitations, the images received are of low resolution and quality, and may contain compression artifacts. To combat this, we developed a new direct vision task feature pyramid recovery method with a plug-and-play framework that does not require retraining of the vision task network.

To achieve low-complexity and high-efficiency feature pyramid recovery, a joint frequency and pixel domain neural learning approach, which we call TSFnet, is introduced. Instead of recovering dense pixels, which is usually much harder, sparse vision task features are easier to recover via a residual learning framework. TSFnet utilizes a dual spatial and frequency branch based tokenization of the input image sensor field, and introduces deep fusion blocks with channel-wise transformers. The {Q, K} dimensions of the channel-wise transformer are carefully managed for deeper networks with a small network size, which allows for a very efficient and effective learning solution.

The approach has had many successes in problems such as SIFT detection from event cameras, joint deblurring and target detection, as well as very low bit rate complex SAR image compression for phase recovery.

Biography

Zhu Li is a professor with the Department of Computer Science & Electrical Engineering, University of Missouri, Kansas City (UMKC), and the director of the NSF I/UCRC Center for Big Learning (CBL) at UMKC. He received his Ph.D. in Electrical & Computer Engineering from Northwestern University in 2004.

He was AFRL summer faculty at the UAV Research Center, U.S. Air Force Academy (USAFA), 2016–2018 and 2020–2024. He was Senior Staff Researcher with Samsung Research America's Multimedia Standards Research Lab in Richardson, TX, 2012–2015, Senior Staff Researcher with FutureWei (Huawei) Technology's Media Lab in Bridgewater, NJ, 2010–2012, Assistant Professor with the Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China from 2008 to 2010, and a Principal Staff Research Engineer with the Multimedia Research Lab (MRL), Motorola Labs, from 2000 to 2008.

His research interests include point cloud and light field compression, graph signal processing and deep learning in next-generation visual compression, remote sensing, image processing and understanding. He has 70+ issued or pending patents and 200+ publications in book chapters, journals, and conferences in these areas. He is an IEEE Senior Member, Associate Editor-in-Chief (2020–2023) and Senior Area Editor (2024–) for IEEE Transactions on Circuits and Systems for Video Technology, Associate Editor for IEEE Transactions on Image Processing (2020–), IEEE Transactions on Multimedia (2015–2018), and IEEE Transactions on Circuits and Systems for Video Technology (2016–2019). His team won the AFRL-sponsored Perception Beyond Visual Spectrum (PBVS) grand challenge at CVPR 2023 on SAR image recognition, and thermal image super-resolution in 2024. He also received the Best Paper Award at IEEE International Conference on Multimedia & Expo (ICME), Toronto, 2006, and IEEE International Conference on Image Processing (ICIP), San Antonio, 2007.