第二十四屆國際模式識別大會（24th International Conference on Pattern Recognition, ICPR 2018）將于2018年8月20日至24日在北京國家會議中心召開，這是其創辦40多年來第一次在中國內地召開。本次大會是由國際模式識別聯合會、中國自動化學會、中國科學院自動化研究所主辦，屆時模式識別、機器學習、計算機視覺等相關領域海內外知名專家共聚一堂，交流相關研究領域的最新成果和發展趨勢。
本屆會議主辦權角逐：國際模式識別大會起始于1972年，是國際模式識別聯合會（IAPR）組織的模式識別領域的旗艦學術會議，每兩年召開一次，主辦國家或地區由國際模式識別聯合會的理事會提前四年經無記名投票決定。在2014年國際模式識別大會的理事會全體會議上，中國和澳大利亞圍繞2018年國際模式識別大會舉辦權展開了角逐。中國科學院自動化研究所模式識別國家重點實驗室學術委員會主任譚鐵牛院士和實驗室主任劉成林研究員代表中國做申辦報告并回答了理事會的質詢，最終贏得理事會青睞。
本屆會議主題、特邀報告及神秘嘉賓：本次會議分為6個主題（模式識別和機器學習、計算機視覺、語音圖像視頻和多媒體、生物識別技術和人機交互、文檔分析和識別以及生物醫學成像和生物信息學）。除了口頭報告和海報展示，大會很榮幸的邀請到6位演講者做主題演講(Zhi-Hua Zhou，Long Quan，Jianchang Mao, K. Venkatesh Prasad, Ashok Popat, Alison Noble)。還會有3位獲得IAPR榮譽獎項的嘉賓(King Sun Fu prize: Matti Pietikainen, J.K. Aggarwal prize: Kristen Grauman, Maria Petrou prize: Rita Cucchiara)。
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King Sun Fu獎獲得者：Matti Pietikainen，教授，奧盧大學，芬蘭
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J.K. Aggarwal獎獲得者：Kristen Grauman，教授，德克薩斯大學奧斯汀分校，美國
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Maria Petrou獎獲得者:Rita Cucchiara，教授，摩德納大學，意大利
特邀報告專家信息：
Long QUAN
Hong Kong University of Science and Technology, China
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Title: The Challenges of 3D Reconstruction with Deep Learning
Abstract:
In this talk, I will review the developments in computer vision and visual learning over the past. Then, I will turn the focus on recent exciting work in deep visual learning and 3D reconstruction breakthrough in computer vision. Here, I showcase the reconstruction approaches in large-scale of hundreds of square kilometers high-rise metropolitan areas and undeveloped rural areas from drones, and in small-scale daily objects from smartphones. I also demonstrate the online cloud platform and portal www.altizure.com with its crowd-sourced Altizure Earth, developed and funded by the HKUST team, rivaling the popular Google Earth!
Biography:
Long Quan received the Ph.D. in Computer Science at INRIA, France, in 1989. Before joining the Department of Computer Science at the Hong Kong University of Science and Technology (HKUST) in 2001 to found his computer vision group, he has been one of the founding members of INRIA Grenoble Computer Vision Group since 1990.
He directed the founding best French PhD thesis in computer science by Peter Sturm, le prix de these Gilles Kahn in 1998, the Piero Zamperoni Best Student Paper Award in 2000 by Maxime Lhuillier, the first of six highlights of SigGraph 2007, the Best Student Poster Paper of CVPR 2008. His many graduate students are now world computer vision leaders at INRIA and CNRS in France, Lund University in Sweden, NUS in Singapore, Beijing University, Alibaba and DJI in China, SFU in Canada, and Microsoft, Google, and Princeton in USA.
He has served in all the major computer vision journals, as an Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), a Regional Editor of Image and Vision Computing Journal (IVC), an editorial board member of the International Journal of Computer Vision (IJCV), an editorial board member of the Electronic Letters on Computer Vision and Image Analysis (ELCVIA), an associate editor of Machine Vision and Applications (MVA), and an editorial member of Foundations and Trends in Computer Graphics and Vision.
He has contributed to all the major computer vision conferences, IEEE International Conference on Computer Vision (ICCV), European Conference on Computer Vision (ECCV), and IEEE Computer Vision and Pattern Recognition (CVPR), and IAPR International Conference on Pattern Recognition (ICPR). He served as a Program Chair of ICPR 2006 Computer Vision and Image Analysis, a Program Chair of ICPR 2012 Computer and Robot Vision, a General Chair of the ICCV 2011 in Barcelona, and a General Chair of the IEEE CVPR 2022 in New Orleans. He is the founding director of the HKUST Center for Visual Computing and Image Science. He is also an IEEE Fellow of the Computer Society.
Most recently, with his HKUST graduates, he founded altizure.com, the world's first portal for generating 3D from drone and smartphone photos!
K. Venkatesh Prasad
Ford Motor Company, USA
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Title: Automobiles and Mobility Solutions
Abstract:
As human intelligence, imagination & ingenuity continue to create advancements in machine-intelligence, we have new ways to serve the mobility needs of our planet. With a world population of about 7.6 billion and immense human and machine intelligence at our disposal, we have the opportunity to create novel experiences and related services associated with traveling from “A” to “B.” Thanks in no small part to advancements in pattern recognition, computer vision and image processing, automobiles are getting “smart” and growing more aware of their surroundings. The world is also getting “smart.” In this talk, we outline some key applications areas of machine intelligence to applications, in the context of addressing human mobility needs.
Biography:
K. Venkatesh Prasad is the Senior Technical Leader for Mobility and a member of the Ford Technology Advisory Board for Open Innovation. Prior to this role, he was Ford’s Global Innovation Implementation Leader, Vehicle Components & Systems Engineering and during a 3-year period help establish eight makerspaces for employee-innovation across global engineering centers. In the earlier years, Prasad applied computer vision, based on early CMOS cameras, to several automobile applications including automatic headlamp detection. In 2011, Prasad architected OpenXC, the industry’s first open-source hardware and open-source software platform, an “innovator’s toolkit,” which launched in 2013 and today is one of the tools used by Ford employee-innovators to design, test and release products and by researchers and experimenters the world over. He also co-founded Ford’s startup-lab in 2012 as a 5-person office; a year later, it scaled to become Ford’s Innovation Center Palo Alto and today is a 150-person operation. Prasad earned a Ph.D. in electrical and computer engineering from Rutgers University in 1990, an M.S. from Washington State University, and engineering degrees from IIT-Madras and NIT-Trichy in India. He has more than 25 years of collaborative experience with universities, startups, automotive suppliers and technology firms. He has co-edited three issues of the Proceedings of the IEEE (on Automotive Technologies; Aerospace and Automotive Software and Cyber-Physical Systems). Prior to coming to Dearborn, Michigan, in 1996, Prasad worked in Menlo Park, California (at Ricoh Innovations) and before that in Pasadena, California (at Caltech and, as a faculty affiliate, at the NASA Jet Propulsion Laboratory).
Jianchang Mao, Microsoft, USA
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Title: Achieving Human Parity Performance in Pattern Recognition and Language Understanding by Machines
Abstract:
For more than a half century, computer scientists have been attempting to train computer systems to perform human perception and cognition tasks, such as, recognize image and speech, comprehend text, translate languages, etc. But until recently those systems were plagued with stagnated accuracies that were far below human performance. In recent years, with the breakthroughs in Deep Learning, advances in the state-of-the-art performance of those systems have gained a strong momentum, thanks to the rapid increase in computing power, big data, and advances in machine learning algorithms. Today, AI breakthroughs are coming at an accelerated pace. The performance of computer systems on several perception and cognition tasks has reached human parity. For example, in 2015 Microsoft researchers achieved 96% accuracy in the ImageNet Computer Vision Challenge, which is as good as a Stanford graduate student. Less than a year later, Microsoft's speech recognition system achieved 5.1% error rate on the Switchboard dataset, which is at parity with professionals who do transcription! In January 2018, Microsoft was the first to achieve human parity in text comprehension tasks on the Stanford Question Answering Dataset. And two months later, Microsoft announced that it reached human parity in English-to-Chinese and Chinese-to-English machine translation on the news dataset. In this talk, I will briefly describe our journey to achieving human parity on these tasks and the technologies that enabled the breakthroughs. I will also present other applications of Deep Learning, such as OCR in unconstrained environments and Advertising.
Biography:
Dr. Jianchang (JC) Mao is Corporate Vice President of Bing Ads Marketplace & Serving, Artificial Intelligence & Research division at Microsoft. He leads a global team of engineers, scientists, product managers, marketplace operators, and analysts, responsible for building technologies and products, and running multi-billion-dollar advertising marketplace that powers Bing, Yahoo!, AOL, and other syndication partners.
Prior to joining Microsoft, Mao was Vice President and Head of Advertising Sciences at Yahoo! Labs, overseeing the R&D of advertising technologies and products. He was also the science and engineering director responsible for the development of backend technologies for several Yahoo! social search products, including Yahoo! Answers. At Yahoo!, Mao received the Leadership Superstar Award in 2010, and received a Superstar Team Award in 2008. Prior to joining Yahoo!, Mao was director of emerging technologies and principal architect at Verity Inc., a leader in Enterprise Search (acquired by Autonomy and then acquired by HP), from 2000 to 2004. Mao began his career as a research staff member at the IBM Almaden Research Center from 1994 to 2000, after receiving his PhD degree in computer science from Michigan State University in 1994.
Mao's research interests include AI, machine learning, data mining, information retrieval, computational advertising, pattern recognition, and image processing. He has published more than 50 papers in journals, book chapters, and conferences, and holds 29 U.S. patents. Mao received an Honorable Mention Award in ACM KDD Cup 2002 (Task 1: Information Extraction from Biomedical Articles), an IEEE Transactions on Neural Networks Outstanding Paper Award in 1996 (for his 1995 paper), and an Honorable Mention Award from the International Pattern Recognition Society in 1993. Mao is a Fellow of IEEE.
Ashok Popat, Google, Inc., USA
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Title: Advice to a Promising OCR Researcher
Abstract:
Document Analysis and Recognition remains a vibrant and challenging field, spanning and touching several domains, including pattern recognition, computer vision, linguistics, digital humanities, and augmented reality. Probably most of the best work in this field remains to be done. That work will build on what came before -- in terms of techniques and understanding already achieved, but also by learning from the best practices of our colleagues and predecessors. As an OCR researcher, in this talk I'll try to reflect on some of the advice I've received from mentors, colleagues, and others in various places, including MIT, Xerox PARC, and Google. I'll present the ideas in the context of developing an Optical Character Recognition system at Google.
Biography:
Ashok C. Popat received the SB and SM degrees from the Massachusetts Institute of Technology in Electrical Engineering in 1986 and 1990, and a PhD from the MIT Media Lab in 1997. He is a Research Scientist at Google in Mountain View, California. At Google he has worked on several projects, including Books, Translate, and (most recently) Optical Character Recognition (OCR). He is part of a team that has developed an OCR system that can handle more than 200 languages, many of which are currently supported through the Cloud Vision web-based API. Prior to joining Google in 2005 he worked at Xerox PARC with Gary Kopec and Henry Baird, on Document Image Decoding. Between 2002 and 2005 he was also a consulting assistant professor of Electrical Engineering at Stanford, where he co-taught (with Dan Bloomberg) a course "Electronic documents: paper to digital." He has also worked at Motorola, Hewlett Packard, PictureTel, and the EPFL in Switzerland. His areas of interest include signal processing, data compression, and pattern recognition. He enjoys running, skiing, sailing, hiking, and spending time with his wife and two daughters.
Zhi-Hua Zhou
Nanjing University, China
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Title: An Exploration to Non-NN Style Deep Learning
Abstract:
Deep learning is a hot topic during the past few years. Generally, the word "deep learning" is regarded as a synonym of "deep neural networks (DNNs)". In this talk, we will discuss on essentials in deep learning and claim that it is not necessarily to be realized by neural networks. We will then present an exploration to non-NN style deep learning, where the building blocks are non-differentiable modules and the training process does not rely on backpropagation.
Biography:
Zhi-Hua Zhou is a Professor of Nanjing University, China. He is the Head of the Department of Computer Science and Technology, Dean of the School of Artificial Intelligence, and Founding Director of the LAMDA Group. His main research interests are in artificial intelligence, machine learning and data mining. He authored the books "Ensemble Methods: Foundations and Algorithms (2012)" and "Machine Learning (in Chinese, 2016)", and published more than 150 papers in top-tier international journals/conferences. According to Google Scholar, his publications have received more than 30,000 citations, with an H-index of 85. He also holds 22 patents and has rich experiences in industrial applications. He has received various awards, including the National Natural Science Award of China, PAKDD Distinguished Contribution Award, IEEE ICDM Outstanding Service Award, etc. He serves as the Executive Editor-in-Chief of Frontiers of Computer Science, and Action/Associate Editor of Machine Learning, IEEE PAMI, ACM TKDD, etc. He was Associate Editor of ACM TIST, IEEE TKDE, IEEE TNNLS, IEEE TCDS, etc. He founded ACML (Asian Conference on Machine Learning) and served as General Chair of IEEE ICDM 2016, Program Chair of IJCAI 2015 Machine Learning track, etc. He will serve as Program Chair of AAAI 2019 and IJCAI 2019. He is the Chair of CCF-AI, and was Chair of the IEEE CIS Data Mining Technical Committee. He is a foreign member of the Academy of Europe, and a Fellow of the ACM, AAAI, AAAS, IEEE, IAPR, CCF and CAAI.
Alison Noble, University of Oxford, UK
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Title: Human Intelligence, Artificial Intelligence and How They Are Changing Ultrasound Image Analysis
Abstract:
Ultrasound imaging is widely used in clinical practice but requires expertise to acquire images and interpret them. Recent advances in machine learning applied to imaging are changing the way we can analyse ultrasound images and extract clinically useful information from ultrasound images and video. Ultrasound images are, after all, “just” spatial maps of acoustic patterns so we would hope that the pattern-recognition power of machine learning would be well-suited for their analysis. In this talk I will describe some recent work of my group on machine learning applied to ultrasound image analysis, some of the interesting challenges specific to this application domain, and highlight some emerging topics of research interest.
Biography:
Professor Alison Noble is the Technikos Professor of Biomedical Engineering at the Institute of Biomedical Engineering, University of Oxford UK. She is best known for her group’s research on ultrasound image analysis much of which has involved inter-disciplinary collaborators with clinical partners. Her current interests are in machine learning applied to ultrasound imaging with application to fetal medicine in the developed world and LMICs, ranging from developing next generation tools for non-expert users of ultrasound technology, to point-of-care computer-assisted basic ultrasound assessment. Throughout her career she has maintained a keen interest in the commercialization of scientific research as a pathway to realizing impact of academic research. She co-founded and is a consultant to Intelligent Ultrasound Ltd, which became part of MedaPhor Group PLC in 2017.
Professor Noble served as the President of the Medical Image Computing and Computer-Assisted Interventions (MICCAI) Society from 2013-16. She is a European Research Council Advanced Research award holder. She is a Fellow of the Royal Academy of Engineering (2008) and a Fellow of the Royal Society (2017) and was awarded an OBE for services to science and engineering in the Queen’s Birthday Honours 2013.
大會贊助商：
白金贊助商：
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金牌贊助商：
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銅牌贊助商：
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目前，大會報名通道已開通，訪問http://www.icpr2018.org可進行報名注冊，歡迎廣大國內外學者及相關領域各界人士參會。
大會組委會：
General Chairs（大會主席）:
Tieniu Tan (China)
Josef Kittler (UK)
Anil Jain (USA)
Program Chairs（程序主席）:
Cheng-Lin Liu (China)
Rama Chellappa (USA)
Matti Pietik?inen (Finland)
Local Arrangements Chair（本地組委會主席）:
Liang Wang (China)
Finance Chair（財政主席）:
Jianhua Tao (China)
International Liaison Chair（外聯主席）:
Gunilla Borgefors (Sweden)
Invited Speakers Chairs（特邀報告主席）:
Katsushi Ikeuchi (China)
Denis Laurendeau (Canada)
Ingela Nystrom (Sweden)
Workshop Chairs（研討會主席）:
David Suter (Australia)
Zhaoxiang Zhang (China)
Yingli Tian (USA)
Tutorial Chairs（講習班主席）:
Greg Mori (Canada)
Zhouchen Lin (China)
Contest Chairs（競賽主席）:
Dimosthenis Karatzas (Spain)
Xiang Bai (China)
Publicity Chairs（宣傳主席）:
David Doermann (USA)
Jean-Marc Ogier (France)
Umapada Pal (India)
Publication Chairs（出版主席）:
Daniel Lopresti (USA)
Ran He (China)
Sponsorship and Exhibitions Chairs（贊助與展覽主席）:
Yasushi Yagi (Japan)
Qiang Ji (USA)
Andreas Dengel (Germany)
Tao Wang (China)
Local Arrangement Committee Members（本地組委會成員）:
Junliang Xing, NLPR, CASIA
Bin Fan, NLPR, CASIA
Shibiao Xu, NLPR, CASIA
Tianzhu Zhang, NLPR, CASIA
Jing Dong, NLPR, CASIA
來源：中國自動化學會
