Impact Score 1.81
Dr. Xun Yang, Research Fellow, National University of Singapore, Singapore ([email protected])Dr. Mingliang Xu, Professor, Zhengzhou University, China ([email protected])Dr. Christopher Thomas, Postdoctoral Researcher, Columbia University, United States of America ([email protected]) Dr. Stevan Rudinac, Associate Professor, Amsterdam Business School, University of Amsterdam, Netherlands ([email protected])Dr. Meng Wang, Professor, Hefei University of Technology, China ([email protected])
With the proliferation of the Internet and the growing prevalence of smart devices and social media, huge amounts of multimedia data (e.g. pictures, audio, videos, and text) are being produced all the time. The scale and richness of multimedia data in terms of content, context and users, facilitated significant breakthroughs in deep learning techniques for a wide range of multimedia computing tasks, such as indexing, search, recommendation and summarization. In some fields, such as automatic speech recognition and visual object recognition, the computers have shown a strong record of besting humans. Despite the significant improvement on the accuracy of multimedia systems, the studying on the trustability of multimedia systems is more challenging and still in the early research stage. Multimedia big data is not only large-scale, heterogeneous, and multimodal, but also noisy and unbalanced. In particular, the spread of fake and misleading multimedia content on social media has become commonplace in recent years. Due to advancements in technology, the creation of such fake and misleading content in audiovisual form is becoming increasingly straightforward, even with limited technical knowledge and at low cost. How to detect the misinformation in multimedia data? How to design a robust, explainable, safe and privacy-preserving multimedia big data system? These research questions are critically important for multimedia systems, especially in specific domains, such as healthcare services, fintech, and self-driving cars, which have attracted increasing attention from multiple research communities. Improving the robustness, explainability, and fairness of multimedia systems should be a crucial step towards making trustworthy decisions.
This special issue aims to bring together researchers interested in defining new and innovative solutions that will advance the research of trustworthy multimedia big data processing and analysis. The goal of the special issue is to solicit high-quality, high-impact and original papers on recent advances about the robustness, explainability, and fairness of multimedia systems. We are interested in submissions covering topics of particular interest include, but are not limited to the following:
Submission InstructionsProspective authors should prepare their manuscript according to the journal's Submission Guidelines at https://www.springer.com/journal/530. Manuscripts should not be published or currently submitted for publication elsewhere. The review process will comply with the standard review process of the Multimedia Systems journal. All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least three independent reviewers.
When submitting to the special issue, authors should select the identifying Article Type in EM as ‘Trustworthy Multimedia Big Data Computing’.
Biography of Guest Editors Xun Yang (https://sites.google.com/site/xunyangprofile/) is currently a Research Fellow at the NExT++ Research Center, National University of Singapore. He received his Ph.D. degree from the Hefei University of Technology, China, in 2017. His current research interests include information retrieval, multimedia content analysis, and computer vision. He regularly serves as the PC member and the invited reviewer for top-tier conferences and prestigious journals in multimedia and artificial intelligence, including ACM Multimedia, CVPR, ICCV, the ACM Transactions on Multimedia Computing, Communications, and Applications (ACM TOMM), the IEEE Transactions on Multimedia (IEEE TMM), the IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), the IEEE Transactions on Knowledge and Data Engineering (TKDE), and the IEEE Transactions on Circuits and Systems for Video Technology (IEEE TCSVT). He also served as the Guest Editors of IEEE TCSVT Special Section on Contextual Object Analysis in Complex Scenes (2020). He is also co-organizing the 1st International Workshop on Adversarial Learning for Multimedia at ACM MM 2021 (October, 2021, Chengdu, China).
Mingliang Xu is a Professor with the School of Information Engineering of Zhengzhou University, China. He received his Ph.D. degree in computer science and technology from the State Key Lab of CAD&CG at Zhejiang University, Hangzhou, China, and the B.S. and M.S. degrees from the Computer Science Department, Zhengzhou University, Zhengzhou, China, respectively. His current research interests include computer graphics, multimedia and artificial intelligence. He has authored more than 40 journal and conference papers in these areas, including ACM TOG, IEEE TPAMI, IEEE TIP, IEEE TCYB, IEEE TCIAIG, ACM SIGGRAPH (Asia), ACM MM, ICCV, etc. He is a member of IEEE and ACM, and the general secretary of ACM SIGAI CHINA CHAPTER.
Christopher Thomas (http://people.cs.pitt.edu/~chris/) is currently a postdoctoral researcher at Columbia University, United States of America, working with Professor Shih-Fu Chang. His interests can broadly be described as high-level image understanding, as well as its intersection with natural language. He received his Ph.D. in Computer Science from the Department of Computer Science at the University of Pittsburgh in 2020.
Stevan Rudinac (https://stevanrudinac.com/) is an Associate Professor of artificial intelligence for business at the University of Amsterdam. He holds a PhD degree in computer science from Delft University of Technology. He has worked as a researcher at the University of Belgrade, Eindhoven University of Technology, and the Netherlands Forensic Institute. In his research he aims at enabling multimedia analytics based on the relevance criteria defined at a higher semantic level, by jointly analyzing visual content and the heterogeneous information associated with it, ranging from text and automatically generated metadata to information about users and their social network. His research focuses on urban computing and business applications.
Meng Wang (https://sites.google.com/view/meng-wang/home) is a Professor with the School of Computer Science and Information Engineering, Hefei University of Technology, China. He is a Fellow of IEEE and IAPR. He received his B.E. degree and Ph.D. degree in the Special Class for the Gifted Young and the Department of Electronic Engineering and Information Science from the University of Science and Technology of China (USTC), Hefei, China, in 2003 and 2008, respectively. His current research interests include multimedia content analysis, computer vision, and pattern recognition. He has authored more than 200 book chapters, journal and conference papers in these areas. He is the recipient of the ACM SIGMM Rising Star Award 2014. He has extensive editorial experience, including serving as an associate editor of IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), IEEE Transactions on Circuits and Systems for Video Technology (IEEE TCSVT), IEEE Transactions on Multimedia (IEEE TMM), and IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS). He is the General Co-Chair of ICMR 2021, PCM 2018 and MMM 2013, and the Program CoChair of ICIMCS 2013.