Immersive Video/Cinematic Virtual Reality
Stanford University, USA
BIOSKETCH Bernd Girod is the Robert L. and Audrey S. Hancock Professor of Electrical Engineering at Stanford University, California. He also serves as Director of the Stanford Center for Image Systems Engineering (SCIEN), the Max Planck Center for Visual Computing and Communication, and as Founding Director Emeritus and now Chair of the Advisory Board of the David and Helen Gurley Brown Institute for Media Innovation, a bicoastal institute between Stanford and Columbia University in New York City. He has also served as a Senior Associate Dean of the Stanford School of Engineering from 2012 to 2016.
He received his M. S. degree in Electrical Engineering from Georgia Institute of Technology, in 1980 and his Doctoral degree from University of Hannover, Germany, in 1987. He joined Massachusetts Institute of Technology, Cambridge, MA, USA, and was an Assistant Professor at the MIT Media Laboratory until 1990. From 1990 to 1993, he was Professor of Computer Graphics and Technical Director of the Academy of Media Arts in Cologne, Germany, jointly appointed with the Computer Science Section of Cologne University. From 1993 until 1999, he held the Chair of Electrical Engineering / Telecommunications at University of Erlangen-Nuremberg, Germany, and was the Head of the Telecommunications Institute I and director of the Telecommunications Laboratory. He served as Chair of the Electrical Engineering Department from 1995 to 1997.
Professor Girod’s research over the course of more than three decades has spanned a broad range of topics including image and video coding, networked media systems, and image-based retrieval. He has authored or co-authored one major text-book (printed in 3 languages), five monographs, and over 600 book chapters, journal articles and conference papers, and is a named inventor of over 25 US patents. He has been a member of the IEEE Image and Multidimensional Signal Processing Technical Committee from 1989 to 1997 and has served on the Editorial Boards for several journals in his field, among them as founding Associate Editor for the IEEE Transactions on Image Processing and Area Editor for Speech, Image, Video & Signal Processing of the IEEE Transactions on Communications. He has served on numerous conference committees, e.g., as Tutorial Chair of ICASSP-97 in Munich and again for ICIP-2000 in Vancouver, as General Chair of the 1998 IEEE Image and Multidimensional Signal Processing Workshop in Alpbach, Austria, as General Chair of the Visual Communication and Image Processing Conference (VCIP) in San Jose, CA, in 2001, and General Chair of Vision, Modeling, and Visualization (VMV) at Stanford, CA, in 2004, and General Co-Chair of ICIP-2008 in San Diego, of VCIP 2010 in China, and of the Packet Video Workshop 2013 in San Jose.
For over 25 years, Professor Girod has worked with start-up ventures as founder, investor, director, or advisor. Most notably, he has been a co-founder and Chief Scientist of Vivo Software, Inc., Waltham, MA (1993-98); after Vivo’s aquisition, 1998-2002, Chief Scientist of RealNetworks, Inc. (Nasdaq: RNWK). He has served on the Board of Directors for 8×8, Inc., Santa Clara, CA, (Nasdaq: EGHT) 1996-2004, and for GeoVantage, Inc., Swampscott, MA, 2000-2005. In 2007, he co-founded Dyyno, Inc. Palo Alto, CA. From 2004 to 2007, he also served as Chairman of the Steering Committee of the new Deutsche Telekom Laboratories at the Technical University of Berlin. He has been an angel investor for 20 years, served on numerous advisory boards, and currently advises HearstLab, a corporate incubator for women-led startup companies in New York City.
Professor Girod was elected Fellow of the IEEE in 1998 ‘for his contributions to the theory and practice of video communications’ and a Fellow of EURASIP in 2008. He has been named ‘Distinguished Lecturer’ for the year 2002 by the IEEE Signal Processing Society. He received the the EURASIP Signal Processing Best Paper Award in 2002, the IEEE Multimedia Communication Best Paper Award in 2007, the EURASIP Image Communication Best Paper Award in 2008, the EURASIP Signal Processing Most Cited Paper Award in 2008, as well as the EURASIP Technical Achievement Award in 2004 and the Technical Achievement Award of the IEEE Signal Processing Society in 2011. The German National Academy of Sciences (Leopoldina) inducted him as a member 2007. He was elected to the National Academcy of Engineering in 2015 for “For contributions to video compression, streaming, and multimedia systems.”
Perception of Visual Sentiment: From Experimental Psychology to Computational Modeling
School of Computing, National University of Singapore, Singapore
ABSTRACT A picture is worth a thousand words. Visual representation is one of the dominant forms of social media. The emotions that viewers feel when observing a visual content is often referred to as the content’s visual sentiment. Analysis of visual sentiment has become increasingly important due to the huge volume of online visual data generated by users of social media. Automatic assessment of visual sentiment has many applications, such as monitoring the mood of the population in social media platforms (e.g., Twitter, Facebook), facilitating advertising, and understanding user behavior. However, in contrast to the extensive research on predicting textual sentiment, relatively less work has been done on sentiment analysis of visual content. In contrast to textual sentiment, visual sentiment is more subjective and implicit. There exists significant semantic gap between high-level visual perception and low-level computational attributes.
In this talk, we argue that these challenges can be addressed by combining the findings from the psychology and cognitive science domain. We will show that a deeper understanding of human perception helps create better computational models. To support that thesis, we will first briefly overview our human-centric research framework, which focuses on applying the paradigms and methodologies from experimental psychology to computer science: First, we collect visual data with human perception through online or lab-controlled psychophysics studies. Then we use inferential statistics to analyze the psychophysics data and model human perception empirically. We then design computational models based on the empirical findings.
We will present three works on visual sentiment in our lab, guided by this research framework. In our first work, we aim to understand human visual perception in a holistic way. We first fuse various partially overlapping datasets with human emotion. We build an empirical model of human visual perception, which suggests that six different types of visual perception (i.e., familiarity, aesthetics, dynamics, oddness, naturalness, spaciousness) significantly contribute to human’s positive sentiment (i.e., liking) of a visual scene.
In our second work, we investigate the relation between human attention and visual sentiment. We build a unique emotional eye fixation dataset with object and scene-level human annotations, and exploit comprehensively how human attention is affected by emotional properties of images. Further, we train a deep convolutional neural network for human attention prediction on our dataset. Results demonstrate that efficient encoding of image sentiment information helps boost its performance.
Our third work explores how human attention influences visual sentiment. We experimentally disentangle effects of focal information and contextual information on human emotional reactions, then we incorporate related insights into computational models. On two benchmark datasets, the proposed computational models demonstrate superior performance compared to the state-of-the-art methods on visual sentiment prediction.
We will end with future research direction on visual sentiment analysis. Our studies highlight the importance of understanding human cognition for interpreting the latent sentiments behind visual scenes.
BIOSKETCH Mohan Kankanhalli is Provost’s Chair Professor of Computer Science at the National University of Singapore (NUS). He is also the Dean of NUS School of Computing. Before becoming the Dean in July 2016, he was the NUS Vice Provost (Graduate Education) during 2014-2016 and Associate Provost during 2011-2013. Mohan obtained his BTech from IIT Kharagpur and MS & PhD from the Rensselaer Polytechnic Institute.
His current research interests are in Multimedia Computing, Information Security & Privacy, Image/Video Processing and Social Media Analysis. He directs the SeSaMe (Sensor-enhanced Social Media) Centre which does fundamental exploration of social cyber-physical systems which has applications in social sensing, sensor analytics and smart systems. He is on the editorial boards of several journals including the ACM Transactions on Multimedia, Springer Multimedia Systems Journal, Pattern Recognition Journal and Springer Multimedia Tools & Applications Journal. He is a Fellow of IEEE.
Concealing Network Delays in Fast Multi-Player Online Games
Benjamin W. Wah
The Chinese University of Hong Kong, China
BIOSKETCH Prof. Benjamin Wah is the Provost and Wei Lun Professor of Computer Science and Engineering at the Chinese University of Hong Kong. Professor Wah is the Franklin W. Woeltge Emeritus Professor of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign, and is a prominent computer scientist, with expertise in non-linear programming, multimedia signal processing and artificial intelligence. He is a fellow of the Institute of Electrical and Electronics Engineers (IEEE), the Association for Computing Machinery (ACM), and the American Association for the Advancement of Science (AAAS) and has served as the President of IEEE Computer Society. Professor Wah has received numerous international honours and awards for his distinguished academic and professional achievements. Among these are the Distinguished Alumni Award in Computer Science of the University of California, Berkeley, the W. Wallace McDowell Award, the Tsutomu Kanai Award and the Richard E. Merwin Distinguished Service Award of the IEEE Computer Society.
In 1998-99, Professor Wah was Professor of Computer Science and Engineering at CUHK, and in that year received an Exemplary Teaching Award. His bonds with the University continued afterwards as he served in the capacity of Adjunct Professor in the Department from 1999 to 2003.
Professor Wah has also long been committed to enhancing the development of higher education and research in Hong Kong. He was a member of the Research Grants Council of the University Grants Committee in Hong Kong between 2005 and 2009, and served as the Chairman of its Engineering Panel between 2006 and 2009. He was re-appointed as member of the RGC in October 2011 and appointed as Chairman of the RGC in January 2013. He was appointed as ex-officio member of the Advisory Committee on Innovation and Technology of the HKSAR Government in the capacity as the Chairman of the Research Grants Council in 2015. He is currently member of various UGC Sub-Committees, including Strategy Sub-Committee and Research Group. He also serves on the Innovation and Technology Advisory Committee of the Hong Kong Trade Development Council.
Born and brought up in Hong Kong, Professor Wah graduated from Queen Elizabeth School and pursued further studies in the US. He received his BS and MS in Electrical Engineering and Computer Science from Columbia University, and his MS in Computer Science and PhD in Engineering from the University of California, Berkeley. He began teaching in Purdue University in 1979, and later joined the University of Illinois at Urbana-Champaign in 1985. He also served as Director of the Advanced Digital Sciences Centre established by the University of Illinois in Singapore in 2009, with funding from the Singapore government’s Agency for Science, Technology and Research.
On Mining Brain Images for Neurological Disorder Detection
Philip S. Yu
University of Illinois at Chicago, USA
BIOSKETCH Philip S. Yu’s main research interests include big data, data mining (especially on graph/network mining), social network, privacy preserving data publishing, data stream, database systems, and Internet applications and technologies. He is a Distinguished Professor in the Department of Computer Science at UIC and also holds the Wexler Chair in Information and Technology. Before joining UIC, he was with IBM Thomas J. Watson Research Center, where he was manager of the Software Tools and Techniques department. Dr. Yu has published more than 970 papers in refereed journals and conferences with more than 74,500 citations and an H-index of 127. He holds or has applied for more than 300 US patents.
Dr. Yu is a Fellow of the ACM and the IEEE. He is the recipient of ACM SIGKDD 2016 Innovation Award for his influential research and scientific contributions on mining, fusion and anonymization of big data, the IEEE Computer Society’s 2013 Technical Achievement Award for “pioneering and fundamentally innovative contributions to scalable indexing, querying, searching, mining and anonymization of big data”, and the Research Contributions Award from IEEE Intl. Conference on Data Mining (ICDM) in 2003 for his pioneering contributions to the field of data mining. He also received an IEEE Region 1 Award for “promoting and perpetuating numerous new electrical engineering concepts” in 1999. He had received several UIC honors, including Research of the Year at 2013 and UI Faculty Scholar at 2014. He also received many IBM honors including 2 IBM Outstanding Innovation Awards, an Outstanding Technical Achievement Award, 2 Research Division Awards and the 94th plateau of Invention Achievement Awards. He was an IBM Master Inventor.
Dr. Yu is the Editor-in-Chief of ACM Transactions on Knowledge Discovery from Data. He is on the steering committee of ACM Conference on Information and Knowledge Management and was a steering committee member of the IEEE Conference on Data Mining and the IEEE Conference on Data Engineering. He was the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (2001-2004). He had also served as an associate editor of ACM Transactions on the Internet Technology (2000-2010) and Knowledge and Information Systems (1998-2004). In addition to serving as program committee member on various conferences, he was the program chair or co-chairs of the 2009 IEEE Intl. Conf. on Service-Oriented Computing and Applications, the IEEE Workshop of Scalable Stream Processing Systems (SSPS’07), the IEEE Workshop on Mining Evolving and Streaming Data (2006), the 2006 joint conferences of the 8th IEEE Conference on E-Commerce Technology (CEC’ 06) and the 3rd IEEE Conference on Enterprise Computing, E-Commerce and E-Services (EEE’ 06), the 11th IEEE Intl. Conference on Data Engineering, the 6th Pacific Area Conference on Knowledge Discovery and Data Mining, the 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, the 2nd IEEE Intl. Workshop on Research Issues on Data Engineering: Transaction and Query Processing, the PAKDD Workshop on Knowledge Discovery from Advanced Databases, and the 2nd IEEE Intl. Workshop on Advanced Issues of E-Commerce and Web-based Information Systems. He served as the general chair or co-chairs of the 2016 IEEE Intl. Conference on BIGDATA, the 2014 IEEE Intl. Conference on Data Science and Advanced Analytics, the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, the 2012 Pacific-Asia Conference on Knowledge Discovery and Data Mining, the 2009 IEEE Intl. Conf. on Data Mining, the 2009 IEEE Intl. Conf. on Data Engineering, the 2006 ACM Conference on Information and Knowledge Management, the 1998 IEEE Intl. Conference on Data Engineering, and the 2nd IEEE Intl. Conference on Data Mining.
Dr. Yu received the B.S. Degree in E.E. from National Taiwan University, the M.S. and Ph.D. degrees in E.E. from Stanford University, and M.B.A. degree from New York University.