CCF广州珠江论坛丨视觉计算国际学术线上论坛（预告）

报告1：Webly Supervised Fine-Grained Visual Recognition（网络监督的细粒度视觉识别）

Abstract: Labeling objects at the subordinate level typically requires expert knowledge, which is not always available from a random annotator. Accordingly, learning directly from web images for fine-grained visual classification (FGVC) has attracted broad attention. However, the existence of noise in web images is a huge obstacle for training robust deep neural networks. To this end, we propose a novel approach to remove irrelevant samples from the real-world web images during training, and only utilize useful images for updating the networks. Thus, our network can alleviate the harmful effects caused by irrelevant noisy web images to achieve better performance.

嘉宾简介: Dr. Jian Zhang received the Ph.D. degree in electrical engineering from the University of New South Wales (UNSW), Sydney, Australia, in 1999. From 1997 to 2003, he was with the Visual Information Processing Laboratory, Motorola Labs, Sydney, as a Senior Research Engineer, and later became a Principal Research Engineer and a Foundation Manager with the Visual Communications Research Team. From 2004 to July 2011, he was a Principal Researcher and a Project Leader with Data61/NICTA, Sydney. He is currently an Associate Professor and Director of Multimedia Data Analytics Lab with the Global Big Data Technologies Centre, School of Electrical & Data Engineering, Faculty of engineering and Information Technology, University of Technology Sydney, Sydney. Prof Zhang’s research interests include multimedia signal processing, computer vision, pattern recognition, visual information mining, human-computer interaction and intelligent video surveillance systems. Prof Zhang has co-authored more than 150 paper publications, book chapters, patents and technical reports from his research output, he was the co-author of eight granted US and China patents. Dr. Zhang is an IEEE Senior Member and Associated Editor, IEEE Transactions on Multimedia. He was Associated Editor, IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT) from 2006 – 2015, He chaired a number of IEEE International conferences including Technical Program Chair of 2008 IEEE Multimedia Signal Processing Workshop, Leading General Co-Chair, of 2012 International Conference on Multimedia and Expo (ICME) in Melbourne Australia, of 2019 Technical Program Co-Chair of 2014 IEEE International Conference on Visual Communications and Image Processing, Technical Program Co-Chair of IEEE ICME 2020 in London and Leading General Chair of 2019 IEEE International Conference on Visual Communications and Image Processing in Sydney Australia.

报告3：Point Cloud Compression and Communication（点云压缩与通信）

Abstract: Point cloud data arises from depth sensing and capturing for both auto driving/navigation/smart city, as well as immersive content capture and VR/AR playback applications. Recent advances in sensor technology and algorithms, especially 77Ghz MIMO radar systems, and high resolution structured light in conjunction with very high resolution rgb camera arrays, have made point cloud capture getting closer to real world applications. In this talk I will overview the related research at the Multimedia Computing & Communication Lab at UMKC,  discuss the main technical challenges and solutions in point cloud capture, compression and communication for auto driving and smart city applications,  especially the scalable geometry compression, video based point cloud compression (VPCC) problems, and our recent results in advanced 3D motion model, occupancy map driven rate-distortion optimization and multi-attribute compression that significantly advanced the state of art in video based PCC. 

嘉宾简介: Zhu Li is an associated professor with the Dept of CSEE, University of Missouri, Kansas City, USA, directs the NSF I/UCRC Center for Big Learning at UMKC. He received his PhD from Electrical & Computer Engineering from Northwestern University in 2004. He was Sr. Staff Researcher/Sr. Manager with Samsung Research America's Multimedia Core Standards Research Lab in Dallas, from 2012-2015, Sr. Staff Researcher at FutureWei, from 2010-12, Assistant Professor with the Dept of Computing, The HongKong Polytechnic University from 2008 to 2010, and a Principal Staff Research Engineer with the Multimedia Research Lab (MRL), Motorola Labs, Schaumburg, Illinois, from 2000 to 2008. His research interests include image/video analysis, compression, and communication and associated optimization and machine learning problems. He has 46 issued or pending patents, 100+ publications in book chapters, journals, conference proceedings and standards contributions in these areas. He is the Associate Editor-in-Chief for IEEE Trans on Circuits & System for Video Tech, 2020~, and served and serving as Associated Editor for IEEE Trans on Image Processing (2019~), IEEE Trans on Multimedia (2015-18), and IEEE Trans on Circuits & System for Video Tech (2016~19). He received a Best Paper Award from IEEE Int'l Conf on Multimedia & Expo (ICME) at Toronto, 2006, and a Best Paper Award from IEEE Int'l Conf on Image Processing (ICIP) at San Antonio, 2007. 

报告4: Human-centered AI for 3H Care（以人为本的3H护理AI）

Abstract: As a consequence of global population aging, the ratio of the number of healthcare professionals to the number of people in need of care is decreasing rapidly. A major challenge is that chronic diseases will consume an increasing fraction of care resources. Health problems related to hyperglycemia, hypertension, and hyperlipidemia (3H) affect a large segment of the population. For example, one in four Singaporeans suffer from 3H. In this talk, I will share some of the on-going research work at the NTU LILY Research Centre to address 3H challenges. We believe that there is great potential in the application of human-centered AI technologies for early detection and intervention measures which will significantly reduce the incidence of 3H within the whole population. We plan a large-scale study to assess the effectiveness and cost of our approaches.

嘉宾简介: Dr. Di Wang received his Bachelor degree in Computer Engineering and his Ph.D. degree in Computer Science from School of Computer Science and Engineering, Nanyang Technological University, Singapore, in 2003 and 2014, respectively. Prior to joining Nanyang Technological University, Singapore, he had been working in various universities and companies as teaching assistant, project officer, and data analyst. He is currently working as the Senior Research Fellow and the Research Manager in the Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY), Nanyang Technological University, Singapore. Dr Wang has published over forty technical papers in major journals and conferences of his fields. His research interests include computational neuroscience, computational intelligence, autonomous agents, affective computing, ubiquitous computing, etc. Dr Wang is an IEEE Senior Member. He has served as reviewer of major journals and various chair positions and program committee members of major conferences of his fields. In 2019, Dr Wang received a Best Paper Award at the IEEE International Conference on Agents (ICA).