【ICIG2021】Latest News & Announcements of the Tutorial

The 11th International Conference on Image and Graphics (ICIG) will be held in Haikou, China, on 26 – 28, December, 2021. We sincerely invite the researches over the world in this area to join us.

Xi Li

Title: Attention and Transformers for Vision

Abstract: The era of the Internet and the Internet of Things gives rise to big data of images or videos. It is in urgent need of artificial intelligence technologies and methods in order to effectively extract knowledge from these massive vision data. Therefore, in the epoch of knowledge economy, how to perform artificial intelligence-driven visual computing has become a core technical problem that needs to be solved foremost. This lecture mainly focuses on data-driven visual feature learning based on attention and transformers from large-scale image/video data. It analyzes and introduces the main research problems and technical methods involved in large-scale visual feature learning in terms of target visual perception characteristics, visual feature representation, deep learner construction mechanism, high-level semantic understanding. Also, it systematically reviews the development of visual feature representation and learning, and introduces a series of representative works and practical applications in recent years using visual feature learning for visual semantic analysis and understanding. At the end of the lecture，some open problems and difficulties in the learning of visual features will be discussed.

Biography: Prof. Xi Li，IET Fellow，IEEE Senior Member，was the winner of Zhejiang Provincial Science Foundation for Outstanding Young Scholars, and was appointed as a distinguished expert of the Zhejiang Province government. Also, he served as a council member of the China Image and Graphics Society. Meanwhile, he had a wealth of academic experience in serving as program committee members of top conferences (e.g., NIPS, ICML, CVPR, ICCV) or reviewers of premier journals (e.g., IEEE TPAMI, IJCV, IEEE TIP). Besides, he made several invited talks at many well-known conferences at home and abroad (i.e., RACV 2016, ICSW 2017, ICDS 2017, IEEE FMT 2018). His research interests mainly focus on the AI fields of computer vision and machine learning, and has published approximately 150 top conferences and leading journal papers with about 4600 Google Scholar citations. He devoted his efforts to enabling many academic roles in conference organization (e.g., PRCV 2019 AC, ICPR 2018 AC, IJCAI 2019 SPC, and ICCV 2019 AC, CVPR 2020 AC, ICPR 2020 AC) and journal editorial management (e.g., AEs of IEEE TNNLS, IEEE TCSVT, Neurocomputing, and Neural Processing Letters). He won two Best International Conference Paper Awards (including ACCV 2010 and DICTA 2012), an ICIP 2015 Top 10% Conference Paper Award, and an ACML 2017 Best Student Paper Award. In addition, he won two China Natural Science and Technology Awards (including first-class and second-class prizes) and a Chinese Patent Excellence Award.

Wenrui Dai

Title: Convolutional Neural Networks for Signals on Graphs

Abstract: Signals of interest are supported on a graph structure in many real-world applications, including sensor networks, social networks, transportation systems, gene regulatory networks, and 3-D point clouds. It is of great importance to extend standard signal processing tools for representing signals on graphs. With the development of deep learning techniques, graph neural networks (GNNs) generalize the deep convolutional neural networks to signals on graphs and pave a new way for learning node-level and graph-level representations. In this talk, I will present the development and current trends of convolutional neural networks for signals on graphs. I will begin with the fundamental of graph signal processing and then elaborate the graph convolution and pooling operations developed in the spatial and spectral domains. Finally, I will introduce our recent works on multi-scale representation of signals on graphs via graph convolutional neural networks.

Biography: Wenrui Dai received B.S., M.S., and Ph.D. degree in Electronic Engineering from Shanghai Jiao Tong University, Shanghai, China in 2005, 2008, and 2014. He is currently an associate professor at the Department of Computer Science and Engineering, Shanghai Jiao Tong University (SJTU). Before joining SJTU, he was with the faculty of the University of Texas Health Science Center at Houston from 2018 to 2019. He was a postdoctoral scholar with the Department of Biomedical Informatics, University of California, San Diego from 2015 to 2018 and a postdoctoral scholar with the Department of Computer Science and Engineering, SJTU from 2014 to 2015. His research interests include image/signal processing, learning-based image/video coding and predictive modeling. He has published 60 papers in prestigious journals like IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, IEEE Transactions on Signal Processing and conferences like ICML and CVPR.

Conference Website

http://icig2021.csig.org.cn/

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http://conf.csig.org.cn/fair/394

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