PRCV-2022

Keynote

Mu-ming Poo

Member of Chinese Academy of Sciences

Wen Gao

Member of Chinese Academy of Engineering

Yaonan Wang

Member of Chinese Academy of Engineering

Yaonan Wang

Member of Chinese Academy of Engineering

Bio: Yaonan Wang is an Academician of Chinese Academy, and an expert in robotic and intelligence control. He is serving as a professor of Hunan University and the director of RVC-National Engineering Lab. He is currently a CAA Fellow ( Chinese Association of Automation ), a CCF Fellow ( Chinese Computer Federation Fellow) and a CAAI Fellow (Chinese Association for Artificial Intelligence Fellow). He is also the chairman of the council of Chinses Society of Image and Graphics (CSIG), the vice chairman of the council of China Artificial Intelligence Robot Industry Alliance, a standing member of the council of CAA, a member of the supervisor board of CAAI, a member of the executive committee of Artificial Intelligence and Block Chain Technology in Ministry of Education, and the chairman of the council of Hunan Association of Automation. He was the specialist in intelligent robot area of "863 Plan", and the chief scientist of EU Fifth Framework International Cooperation Major Project.He has long been engaged in research and teaching work of robot perception, control technology and engineering application. He won 1 second prize of the National Technological Invention Award, 3 second prizes of the National Science and Technology Progress Award, and 11 first prizes of the provincial /ministerial level award. Moreover, he has published more than 200 papers indexed by SCI, published 15 scientific books, and obtained more than 80 invention parents. He was also selected as a German Humboldt Scholar. Additionally, he has cultivated more than 70 doctoral students, and won the honorary titles of outstanding backbone teachers of national colleges and universities, national May 1st labor medal, national advanced worker, national innovation competition award, and advanced individual in fighting against the covid-19 in Hunan Province.

Title: 机器视觉高光谱检测技术应用与发展趋势

Abstract: 机器视觉感知作为机器人的“高精密眼睛”，其发展对机器人起着重要的作用。高光谱机器视觉感知具备多模态成像系统与智能分析识别功能，是当前机器视觉领域的研究前沿。报告将从研究背景与意义、研究现状与面临挑战、关键技术等方面介绍高光谱机器视觉感知技术应用。首先，以智能机器人在高端制造过程中面临的感知手段有限、测量检测精度低、缺陷样本数量少等挑战为牵引，介绍了高光谱机器视觉研究的总体技术路线与关键科学问题，包括高速高精快照式成像、无监督异常检测方法、跨场景模型部署等方面；最后介绍了高光谱机器视觉未来的发展趋势与展望。

Yirong Wu

Member of Chinese Academy of Sciences

Hong Qiao

Member of Chinese Academy of Sciences

Kyoung Mu Lee

Editor in Chief of the IEEE TPAMI

Kyoung Mu Lee

Editor in Chief of the IEEE TPAMI

Bio: KYOUNG MU LEE (Fellow, IEEE) is currently the Editor in Chief of the IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (TPAMI); He received the B.S. and M.S. degrees in control and instrumentation engineering from Seoul National University (SNU), Seoul, South Korea, in 1984 and 1986, respectively, and the Ph.D. degree in electrical engineering from the University of Southern California, in 1993. He is the director of the Interdisciplinary Graduate Program in Artificial Intelligence at SNU. He is an Advisory Board Member of the Computer Vision Foundation (CVF). He was a Distinguished Lecturer of the Asia-Pacific Signal and Information Processing Association (APSIPA), from 2012 to 2013. He has received several awards, in particular, the Medal of Merit and the Scientist of Engineers of the Month Award from the Korean Government, in 2018 and 2020, respectively; the Most Influential Paper Over the Decade Award by the IAPR Machine Vision Application, in 2009; the ACCV Honorable Mention Award, in 2007; the Okawa Foundation Research Grant Award, in 2006; the Distinguished Professor Award from the College of Engineering of SNU, in 2009; and the SNU Excellence in Research Award in 2020. He has also served as a General Chair for ICCV2019, ACMMM2018, and ACCV2018; a Program Chair for ACCV2012; a Track Chair for ICPR2020 and ICPR2012; and an Area Chair for CVPR, ICCV, and ECCV many times. He has served as an Associate Editor-in-Chief (AEIC) and an Associate Editor for the Machine Vision and Application (MVA) journal, the IPSJ Transactions on Computer Vision and Applications (CVA), and the IEEE SIGNAL PROCESSING LETTERS (SPL); and an Area Editor for the Computer Vision and Image Understanding (CVIU). He is the founding member and served as the President of the Korean Computer Vision Society (KCVS). Prof. Lee is a Fellow of IEEE, a member of the Korean Academy of Science and Technology (KAST) and the National Academy of Engineering of Korea (NAEK).

Title: Toward Real-World Image Super-Resolution: Challenges and Approaches

Abstract: Image Super Resolution (SR) which aims to reconstruct a high-resolution (HR) image from a low-resolution (LR) input, plays an essential role in computer vision, digital photography, and many real applications. Recently, a plethora of SR methods have been developed based on deep CNNs and large-scale datasets. However, most of the state-of-the-art methods still do not generalize well to real-world scenarios even though they perform relatively well on public benchmarks. In this talk, we will address some of the technical issues and challenges in the real-world SR problem including the domain gap, arbitrary scale transformation, and real-time processing issues. And then we introduce new approaches to tackle these challenges by learning unknown real down-sampling process via GAN with new effective losses, allowing generalized Image SR under arbitrary transformation, and optimizing the network structures via adaptive quantization and pruning. We empirically demonstrate that our new strategie

Zhengyou Zhang

Director of AI Lab and Robotics X, Tencent

Alan Yuille

Bloomberg Distinguished Professor of Cognitive Science and Computer Science at Johns Hopkins University

Alan Yuille

Bloomberg Distinguished Professor of Cognitive Science and Computer Science at Johns Hopkins University

Bio: Prof Alan Yuille is a Bloomberg Distinguished Professor of Cognitive Science and Computer Science at Johns Hopkins University. He directs the research group on Compositional Cognition, Vision, and Learning. He is affiliated with the Center for Brains, Minds and Machines, and the NSF Expedition in Computing, Visual Cortex On Silicon. Alan Yuille received the BA degree in mathematics from the University of Cambridge in 1976. His PhD on theoretical physics, supervised by Prof. S.W. Hawking, was approved in 1981. He was a research scientist in the Artificial Intelligence Laboratory at MIT and the Division of Applied Sciences at Harvard University from 1982 to 1988. He served as an assistant and associate professor at Harvard until 1996. He was a senior research scientist at the Smith-Kettlewell Eye Research Institute from 1996 to 2002. He was a full professor of Statistics at the University of California, Los Angeles, as a full professor with joint appointments in computer science, psychiatry, and psychology. He moved to Johns Hopkins University in January 2016. His research interests include computational models of vision, mathematical models of cognition, medical image analysis, and artificial intelligence and neural networks.

Title: What Have Deep Nets ever done for us?

Abstract: A recent opinion paper about the strength and weaknesses of Deep Nets argues that despite the huge successes of deep networks they will be unable to overcome the fundamental problems of computer vision due to the complexity of the real world (Yuille & Liu 2021). This talk revisits these ideas taking into account recent progress in Deep Nets caused by transformers and self-supervised learning. We argue that the fundamental problems of vision remain and the limitations of current algorithms are obscured by the way the vision community evaluates performance. We argue for more challenging evaluation criteria, such as out-of-distribution testing, adversarial examiners, and the construction of more challenging evaluation datasets. We argue that approaches that build generative models of the three dimensional world, inspired by properties of the human visual system, are most likely to overcome these challenges.