Draft Full Paper Due:
April 15, 2022

Notification of Draft Paper Acceptance:
April 28, 2022

Final Manuscript Due:
May 05, 2022

Author Registration Deadline:
May 05, 2022

Conference Dates:

May 27-29, 2022

 

Home > Keynote Speakers
KEYNOTE SPEAKERS

Prof. Wangmeng Zuo (H-index: 78)

Harbin Institute of Technology, China

Wangmeng Zuo is currently a Professor in the School of Computer Science and Technology, Harbin Institute of Technology. He received the Ph.D. degree in computer application technology from the Harbin Institute of Technology, Harbin, China, in 2007. His current research interests include image enhancement and restoration, image and face editing, object detection, visual tracking, and image classification. He has published over 100 papers in top tier academic journals and conferences. His publications have received 30,000+ citations in terms of Google Scholar. He has served as Area Chairs for ICCV 2019, CVPR 2020/2021, ECCV 2022, and is currently an Associate Editor of IEEE Trans. Pattern Analysis and Machine Intelligence, and IEEE Trans. Image Processing.

Speech Title: "Learning Deep Image Restoration Network: From Full- and Self-supervision to Non-ideal Supervision"

Abstract: Deep learning has made unprecedented success in many computer vision tasks, which largely depends on massive well annotated data. However, the annotation cost is expensive and laborious, making unsupervised/self-supervised learning as feasible alternatives for training deep networks. In this talk, we first introduce a novel self-supervised method by learning both noise model and deep denoising network. Subsequently, we present a self-supervised adaptation model for adapting a denoising network to new domains and devices. Then, we show that the ideal ground-truth images are not available for deep ISP and Raw super-resolution, and present our alternative solution for learning deep networks with non-ideal supervision (e.g., color inconsistency and spatial deformation). Finally, we discuss the potential to incorporate self-supervised learning and multiple sensors for learning deep image restoration models.

 

Prof. Huiyu Zhou

University of Leicester, UK

Prof. Huiyu Zhou received a Bachelor of Engineering degree in Radio Technology from Huazhong University of Science and Technology of China, and a Master of Science degree in Biomedical Engineering from University of Dundee of United Kingdom, respectively. He was awarded a Doctor of Philosophy degree in Computer Vision from Heriot-Watt University, Edinburgh, United Kingdom. Prof. Zhou currently is a Full Professor at School of Computing and Mathematical Sciences, University of Leicester, United Kingdom. He has published over 400 peer-reviewed papers in the field. He was the recipient of "CVIU 2012 Most Cited Paper Award", “MIUA 2020 Best Paper Award”, “ICPRAM 2016 Best Paper Award” and was nominated for “ICPRAM 2017 Best Student Paper Award” and "MBEC 2006 Nightingale Prize". Dr. Zhou serves as the Editor-in-Chief of Recent Advances in Electrical & Electronic Engineering and Associate Editor of IEEE Transaction on Human-Machine Systems, IEEE Journal of Biomedical and Health Informatics, Pattern Recognition, PeerJ Computer Science, Security and Safety, Scientific Reports, Machine Intelligence Research, International Journal of Image and Graphics and IEEE Access, and Area Chair of IJCAI, ICRA and BMVC. He has given over 100 invited talks at international conferences, industry and universities, and has served as a chair for 70 international conferences and workshops.

Speech Title: "Transform Image Understanding with Artificial Intelligence"

Abstract: There are many questions to answer in image interpretation and understanding. Uncertainty in image analysis needs strong and powerful modelling tools to describe the objects in the images. Artificial intelligence (AI) plays a very important role in the design of a robust tool for image representation. Using some examples from his own research work on uncertainty analysis, he will explore how AI can stimulate new concepts or development of dealing with complicated problems and lead to novel adventures through these applications.