报告人: Professor Moncef Gabbouj（左一）, Tampere, University, Tampere, Finland and Associate Professor Li Yu（右一）, Nanjing University of Information Science and Technology, Nanjing, China
Inviter: Professor Xiong Hongkai, School of Information and Electronic Engineer, ECNU
时 间：2026年5月22日 13：30-15:30
地 点：信息楼魔方厅
报告人简介：
MONCEF GABBOUJ received his BS degree in 1985 from Oklahoma State University, and his MS and PhD degrees from Purdue University, in 1986 and 1989, respectively, all in electrical engineering.Dr. Gabbouj is a Professor of Information Technology at the Department of Computing Sciences, Tampere University, Tampere, Finland. He was Academy of Finland Professor during 2011-2015. His research interests include Big Data analytics, artificial intelligence, machine learning, pattern recognition, and video processing and coding. Dr. Gabbouj is a Fellow of the IEEE and member of the Academia Europaea and the Finnish Academy of Science and Letters. He is the past Chairman of the IEEE CAS TC on DSP and committee member of the IEEE Fourier Award for Signal Processing. He served as associate editor and guest editor of many IEEE, and international journals and Distinguished Lecturer for the IEEE CASS.Dr. Gabbouj served as General Chair of IEEE ICIP 2024, ISCAS 2019, ICIP 2020, and ICME 2021. Gabbouj is Finland Site Director of the USA NSF IUCRC funded Center for Big Learning and led the Artificial Intelligence Research Task Force of Finland’s Ministry of Economic Affairs and Employment funded Research Alliance on Autonomous Systems (RAAS).
Dr. Li Yu (Member, IEEE) received the B.S. degree from Soochow University, Suzhou, China, in 2012, and the Ph.D. degree in electrical engineering and electronics from the University of Liverpool, Liverpool, U.K., in 2017. She is currently an Associate Professor in the School of Computer Science at Nanjing University of Information Science and Technology, Nanjing, China. She was a Postdoctoral Researcher with the Department of Signal Processing, Tampere University of Technology, Tampere, Finland. Her research interests include video streaming, video coding, point cloud upsampling, computer vision, and deep learning.
报告内容介绍：
Rethinking Deep Learning (DL) by reconsidering the core neuron model used in all ANN architectures for image processing applications. DL outperformed many traditional approaches in numerous fields of science. However, DL comes at a price of high computational cost and follows mostly a Blackbox approach. Targeting Green Learning, we aim to develop more computationally efficient Artificial Neural Networks, called Operational Neural Networks as alternatives to conventional Convolutional Neural Networks (CNNs). ONNs can perform any linear or non-linear transformation with a proper combination of “nodal” and “pool” operators. This is a great leap towards expanding the neuron’s learning capacity in CNNs, which thus far required the use of a single nodal operator for all synaptic connections for each neuron. This restriction has been lifted by introducing a superior neuron called the “generative neuron” where each nodal operator can be customized during the training to maximize learning. As a result, the network can self-organize the nodal operators of its neurons’ connections. Self-Organized ONNs (Self-ONNs) equipped with superior generative neurons can achieve diversity even with a compact configuration. We shall explore the use of traditional and advanced neural network models equipped with the generative and the super neuron in several image and video processing applications. Among the applications, we will also focus on video compression, enhancement, streaming and quality assessment.
