In-Country Events (China)

Bio: Guosheng Lin is an Assistant Professor at the School of Computer Science and Engineering, Nanyang Technological University. Prior to that, he was a research fellow at the Australian Centre for Robotic Vision from 2014 to 2017. He received his PhD from the University of Adelaide in 2014. His research interests generally lie in deep learning and 2D/3D visual understanding. He has published over 100 research articles in top-tier research venues and his research work receives over 10K citations as per Google Scholar. He is named in the world's top 2% of scientists List.

​Learning to Appreciate: Transforming Multimedia Communications via Deep Video Analytics July 30, 2022 | 2:00 pm - 3:00 pm (China Standard Time) Prof. Yonggang Wen Nanyang Technological University

Abstract: Media-rich applications will continue to dominate mobile data traffic with an exponential growth, as predicted by Cisco Video Index. The improved quality of experience (QoE) for the video consumers plays an important role in shaping this growth. However, most of the existing approaches in improving video QoE are system-centric and model-based, in that they tend to derive insights from system parameters (e.g., bandwidth, buffer time, etc) and propose various mathematical models to predict QoE scores (e.g., mean opinion score, etc). In this talk, we will share our latest work in developing a unified and scalable framework to transform multimedia communications via deep video analytics. Specifically, our framework consists two main components. One is a deep-learning based QoE prediction algorithm, by combining multi-modal data inputs to provide a more accurate assessment of QoE in real-time manner. The other is a model-free QoE optimization paradigm built upon deep reinforcement learning algorithm. Our preliminary results verify the effectiveness of our proposed framework. We believe that the hybrid approach of multimedia communications and computing would fundamentally transform how we optimization multimedia communications system design and operations.

​Advancing Technologies to Improve Flexible Endoscopy June 30, 2022 | 09:00 a.m. - 10:00 a.m. (China Standard Time) Prof. Louis Phee Nanyang Technological University

Abstract: The first flexible endoscope was developed in the 1960s, shortly after the invention of the optical fiber, which allows light to be transmitted through a flexible fiber. The latest endoscopes use miniature CCD or CMOS cameras to capture clearer images. Besides clearer images, the structure and utility of the flexible endoscopy remained largely unchanged in the past 50 years. High accessibility with minimal damage to healthy tissue is the greatest benefit of the flexible endoscope. It allows for inspection and simple soft tissue manipulation of tubular organs without the need for incisions. The speaker has been developing new technologies for the past 20 years to augment and advance the capabilities of the flexible endoscope. With robotics, intricate surgical procedures can now be performed endoscopically, further blurring the line between conventional surgery and endoscopy. Artificial intelligence and deep learning techniques could improve navigation, tissue manipulation and diagnosis of diseases. Novel sensors and actuators would enable intelligent scopes to be developed that would better understand the environment within the organ of interest and actively conform to different medical scenarios. With the incorporation of these state-of-the-art technologies, the future of flexible endoscopy would see higher accuracy of diagnosis and advanced therapeutic means all delivered from a minimally invasive medical platform that enters the human body via a natural orifice.

Bio: Dr Louis Phee is the Vice President (Innovation & Entrepreneurship) and Dean of College of Engineering at Nanyang Technological University, Singapore. He is also the Tan Chin Tuan Centennial Professor in Mechanical Engineering and a Fellow of the Singapore Academy of Engineers. He graduated from NTU with the B.Eng and M.Eng degrees. He obtained his PhD from Scuola Superiore Sant’Anna, Pisa, Italy in 2002 on a European Union scholarship. His research interests include Medical Robotics and Mechatronics in Medicine. He was a recipient of the prestigious National Research Foundation (NRF) Investigator Award. Professor Phee is the co-founder of 2 NTU start-ups and is an advisor and mentor to entrepreneurial faculty and students. He was awarded the Young Scientist Award, the Outstanding Young Persons of Singapore Award, the Nanyang Outstanding Young Alumni Award, the President’s Technology Award, the Nanyang Innovation and Entrepreneurship Award and the Nanyang Alumni Achievement Award.

​Challenges in Multimodal Conversational Search and Recommendation 27 May 2022 | 9:10 a.m. - 10:10 a.m. (China Standard Time) Prof. Chua Tat-Seng National University of Singapore

Abstract: Information search has been evolving from mostly unidirectional and text-based to interactive and multimodal. Recently, there is also a growing interest in all matters conversational. Multimodal conversation offers users a natural way to query the system by combining text/speech, images/videos and possibly gesture. It also helps to tackle the basic asymmetric problems by injecting conversation to help resolve the ambiguities in search and recommendation. However, the evolution from traditional IR to multimodal conversational search and recommendation (MCSR) faces many challenges. The first set of challenges touches on the basic MCSR models, including how to integrate task-oriented and open domain models, how to model multimodal context and history, and how to integrate domain knowledge and user models. The second set of challenges involves basic interactivity issues, including how to naturally converse using text and visual modalities, how to incorporate intervention strategy into search and browsing; and how to perform interactive IR, QA and recommendation. The third set of challenges looks more into the future on how to build dialogue simulator, and how to make MCSR systems extendable and active by allowing the system and users to co-evolve and becoming more intelligent together. This talk presents current research with pointers towards future research.

Bio: Dr Chua is the KITHCT Chair Professor at the School of Computing, National University of Singapore (NUS). He is also the Distinguished Visiting Professor of Tsinghua University and the Visiting Pao Yue-Kong Chair Professor of Zhejiang University. Dr Chua was the Founding Dean of the School of Computing from 1998-2000. His main research interests include unstructured data analytics, video analytics, conversational search and recommendation, and robust and trustable AI. Dr Chua is the co-Director of NExT, a joint research Center between NUS and Tsinghua, and Sea-NExT, a joint Lab between Sea Group and NExT. Dr Chua is the recipient of the 2015 ACM SIGMM Achievements Award for the Outstanding Technical Contributions to Multimedia Computing, Communications and Applications. He is the Chair of steering committee of Multimedia Modeling (MMM) conference series, and ACM International Conference on Multimedia Retrieval (ICMR) (2015-2018). He was the General Co-Chair of ACM Multimedia 2005, ACM CIVR (now ACM ICMR) 2005, ACM SIGIR 2008, ACM Web Science 2015, ACM MM-Asia 2020, and the upcoming ACM conferences on WSDM 2023 and TheWebConf 2024. He serves in the editorial boards of three international journals. Dr. Chua is the co-Founder of two technology startup companies in Singapore. He holds a PhD from the University of Leeds, UK.

Automated Machine Learning on Graphs 27 May 2022 | 10:10 a.m. - 11:10 a.m. (China Standard Time) Prof. Wenwu Zhu Tsinghua University

Abstract: Automated machine learning (AutoML) on graphs, which combines the strength of graph machine learning and AutoML, is gaining attentions from the research community. This talk will first overview graph machine learning and AutoML on graphs. Then, recent advances, including efficient neural architecture search for self-attention representation, hyper-parameter optimization on large-scale graphs, and increasing explainability in AutoML on graphs, will be discussed. We will also introduce AutoGL, the first dedicated framework and open-source library for AutoML on graphs, which is expected to facilitate the research and application in the community. Last but not least, we discuss multimedia applications of automated graph machine learning and share our insights on future research directions with the audience

Bio: Wenwu Zhu is currently a Professor of Computer Science Department of Tsinghua University and Vice Dean of National Research Center on Information Science and Technology. Prior to his current post, he was a Senior Researcher and Research Manager at Microsoft Research Asia. He was the Chief Scientist and Director at Intel Research China from 2004 to 2008. He worked at Bell Labs New Jersey as a Member of Technical Staff during 1996-1999. He has been serving as the chair of the steering committee for IEEE T-MM since January 1, 2020. He served as the Editor-in-Chief for the IEEE Transactions on Multimedia from 2017 to 2019. And Vice EiC for IEEE Transactions on Circuits and Systems for Video Technology from 2020-2021 He served as co-Chair for ACM MM 2018 and co-Chair for ACM CIKM 2019. His current research interests are in the areas of multimodal big data and intelligence, and multimedia networking. He received 10 Best Paper Awards. He is a member of Academia Europaea, an IEEE Fellow, AAAS Fellow, and SPIE Fellow.

Making the Invisible Visible: Toward High-Quality Deep THz Computational Imaging 27 May 2022 | 11:10 a.m. - 12:10 p.m. (China Standard Time) Prof. Chia-Wen Lin National Tsing Hua University, Taiwan

Abstract: Terahertz (THz) computational imaging has recently attracted significant attention thanks to its non-invasive, non-destructive, non-ionizing, material-classification, and ultra-fast nature for 3D object exploration and inspection. However, its strong water absorption nature and low noise tolerance lead to undesired blurs and distortions of reconstructed THz images. The performances of existing methods are highly constrained by the diffraction-limited THz signals. In this talk, we will introduce the characteristics of THz imaging and its applications. We will also show how to break the limitations of THz imaging with the aid of complementary information between the THz amplitude and phase images sampled at prominent frequencies (i.e., the water absorption profile of THz signal) for THz image restoration. To this end, we propose a novel physics-guided deep neural network design, namely Subspace-Attention-guided Restoration Network (SARNet), that fuses such multi-spectral features of THz images for effective restoration. Furthermore, we experimentally construct an ultra-fast THz time-domain spectroscopy system covering a broad frequency range from 0.1 THz to 4 THz for building up temporal/spectral/spatial/phase/material THz database of hidden 3D objects.

Bio: Prof. Chia-Wen Lin is currently a Professor with the Department of Electrical Engineering, National Tsing Hua University (NTHU), Taiwan. He also serves as Deputy Director of the AI Research Center of NTHU. His research interests include image/video processing, computer vision, and video networking.

Dr. Lin is an IEEE Fellow, and has been serving on IEEE Circuits and Systems Society (CASS) Fellow Evaluating Committee since 2021. He serves as IEEE CASS BoG member-at-Large during 2022-2024. He was Steering Committee Chair of IEEE ICME (2020-2021), IEEE CASS Distinguished Lecturer (2018-2019), and President of the Chinese Image Processing and Pattern Recognition (IPPR) Association, Taiwan (2019-2020). He has served as Associate Editor of IEEE Transactions on Image Processing, IEEE Transactions on Multimedia, IEEE Transactions on Circuits and Systems for Video Technology, and IEEE Multimedia. He also served as a Steering Committee member of the IEEE Transactions on Multimedia. He was Chair of the Multimedia Systems and Applications Technical Committee of the IEEE CASS. He has served as TPC Chair of IEEE ICME in 2010 and IEEE ICIP in 2019, and the Conference Chair of IEEE VCIP in 2018. His papers won the Best Paper Award of IEEE VCIP 2015, and the Young Investigator Award of VCIP 2005.