I C I V I S
W O R K S H O P
Workshop 1:
CG&CAM, Image processing and Pattern recognition, artificial intelligence
Summary: The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of CG&CAM、Image processing and Pattern recognition、artificial intelligence. We encourage prospective authors to submit related distinguished research papers on the subject: theoretical approaches and practical application. Please name the title of the submission email with “paper title_workshp title".
 
Keywords: Image processing and Pattern recognition

                                                                                                                               
Chair : Nianqiang Niu
Shenyang University of technology, China

Professor of Shenyang University of technology. He has been a visiting scholar in Information Engineering at Toyama Prefecture University in Japan. His research interests include CG & CAM, image processing and pattern recognition, artificial intelligence, etc. He has participated in many key scientific and technological projects of the National Natural Science Foundation of China, Liaoning Natural Science Foundation of China , and Liaoning Province, and published many academic papers.
Workshop 2:
Low Quality Image Restoration and Enhancement
Summary: Image restoration, enhancement and manipulation are key computer vision tasks, aiming at the restoration of degraded image content, the filling in of missing information, or the needed transformation and/or manipulation to achieve a desired target (with respect to perceptual quality, contents, or performance of apps working on such images). Recent years have witnessed an increased interest from the vision and graphics communities in these fundamental topics of research. Not only has there been a constantly growing flow of related papers, but also substantial progress has been achieved. Each step forward eases the use of images by people or computers for the fulfillment of further tasks, as image restoration, enhancement and manipulation serves as an important front-end. Not surprisingly then, there is an ever growing range of applications in fields such as surveillance, the automotive industry, electronics, remote sensing, or medical image analysis etc. The emergence and ubiquitous use of mobile and wearable devices offer another fertile ground for additional applications and faster methods. This workshop aims to provide an overview of the new trends and advances in those areas. Moreover, it will offer an opportunity for academic and industrial attendees to interact and explore collaborations.
 
Keywords: Image Restoration, Denoising, Deblurring, Image Super-resolution, High Dynamic Range Imaging

                                                                                                                               
Chair 1: Qingsen Yan
Northwestern Polytechnical University, China

Qingsen Yan, a Professor with the School of Computer Science, Northwestern Polytechnical University (NPU), Xi’an, China. He received the Ph.D. degree in Computer Science and Technology from Northwestern Polytechnical University in Dec. 2019. Before joining NPU, he was a Senior Research Fellow with the Australian Institute for Machine Learning (AIML), Adelaide, Australian. His current research interests include computer vision, multimedia information processing and content understanding, segmentation, remote sensing image interpretation, artificial intelligence, and deep learning. He has published more than 60 academic papers in important international journals and conferences. He has published more than 20 papers in international journals and conferences as the first author, and 1 paper with ESI high citation, including IJCV, IEEE TIP, IEEE TBD, PR, MedIA, IEEE JBHI, and CVPR, AAAI, ACMMM (TOP conference in Computer Vision Field). Google Scholar is more than 1,700, and the relevant research results have authorized 7 Chinese invention patents. He was Runner-up of CVPR22 - High Dynamic Range Image Ghosting Competition, Excellent Doctorial Dissertation Award from CSIG and CIE. He served as Program Committee (PC) of AAAI 2022, and reviewer of TPAMI, TIP, TNNLS, TMM, CVPR, ECCV, ICCV and ACCV. He is a member of the Special Committee on Artificial Intelligence and Pattern Recognition of China Computer Society, a member of the Special Committee on Computer-Aided Design and Graphics of China Computer Society, a member of the Special Committee on Computer Vision of China Computer Society, a member of the Special Committee on Detection, Perception and Imaging of China Graphic and Image Society, a member of the Chinese Society of Electronics, and a member of the Asia-Pacific Society of Artificial Intelligence.

                                                                                                                               
Chair 2: Wei Dong
Xi' an University of Architecture and Technology, China

Wei Dong received the PhD degree from the School of Computer Science, Northwestern Polytechnical University, China, in 2022. He is currently an associate professor at the College of Information and Control Engineering, Xi'an University of Architecture and Technology, China. His research interests include self-supervised learning, graph representation learning, and parameter-efficient fine-tuning. He has been actively publishing his research work on premier international journals and conferences, such as CVPR, NeurIPS, TPAMI, PR, ESWA, KBS, and TII. He is a regular reviewer of CVPR, ICCV, ECCV, and LOG.

Workshop 3:
Image Processing and Computer Vision
Summary: Remote sensing methods are widely used to collect data at a distance from the object under study using a recording device. Their use is rapidly expanding, finding new areas of application with the development of technologies for television, infrared, laser and radar surveillance systems with synthesized apertures. Such systems solve the problems of detecting, isolating and localizing objects of interest in images of various nature. The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of Remote Sensing Systems. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews. Please name the title of the submission email with “paper title_workshp title”.
 
Keywords: remote sensing, image processing, computer vision, object detection and localization

                                                                                                                             
Chair: Vladimir Yurievich Volkov
Saint-Petersburg State Electrotechnical University(LETI); Saint-Petersburg State University of Aerospace Instrumentation

Vladimir Yurievich Volkov received a Dr. Sc. degree (Full doctor) in Computer Science and Technology from The Bonch-Bruevich Saint-Petersburg State University of Telecommunications. He works as a professor at the Dept. of Radioengineering, Saint-Petersburg State Electrotechnical University (LETI) and Saint-Petersburg State University of Aerospace Instrumentation. His research interests include digital image processing for computer vision systems, adaptive, invariant and robust algorithms for signal discrimination and filtering, non-Gaussian models of random signals and fields. Professor V. Yu. Volkov received a certificate of participation in the presidential project "Golden names of higher education". He has more than 200 scientific publications, 22 copyright certificates for inventions and 8 registered computer programs.
Workshop 4:
Stereo Vision, 3D Reconstruction, Scene Understanding, Environmental Perception
Summary: Depth estimation is a very popular research direction in the field of computer vision, playing a crucial role in tasks such as robot vision, 3D reconstruction, augmented reality, and autonomous driving. In recent years, depth estimation methods have received widespread attention and in-depth research as low-level visual tasks. Traditional methods use lidar to obtain depth information, but the cost of obtaining dense and accurate depth maps is too high. In contrast, depth estimation methods based on image/video directly estimate scene depth information, without the need for expensive equipment, which have a wider application space. The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of Depth Estimation. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.
 
Keywords: Stereo Vision, Scene Understanding, Monocular Depth Estimation, Multi-view Depth Estimation

                                                                                                                               
Chair: Wenting Li
Guizhou University of Commerce, China Nanyang Technological University, Singapore

Doctor of Computer Technology and Application, Professor. She received the PhD degree from Macau University of Science and Technology, Macau, China, in June 2017, the MSc degree from GuiZhou University, China, in 2010, the BSc degree from Zhengzhou University of Aeronautics, China, in 2006. She joined the Guizhou University of Commerce, China, in April 2018. Her research interests are intelligent transportation, stereo vision and scene understanding.
Workshop 5:
Artificial Intelligence in Imaging Medicine
Summary: Artificial intelligence in medicine (AIM) mainly uses computer techniques to perform clinical diagnoses and suggest treatments. It aimed to increase the efficiency of medical diagnosis and treatment with the aid of AI systems. AI has the capability of detecting meaningful relationships in multi-modality data-sets and has been widely used in many clinical situations to diagnose, treat, and predict the diseases. AIM has completely changed the traditional model of medicine, significantly improved the level of medical services, and guaranteed human health in various aspects. A broader development prospect for AIM is highly expected nowadays.
 
Keywords: Artificial Intelligence, Medicine, Human Health

                                                                                                                                        
Chair: Xufeng Yao
Shanghai University of Medicineand Health Sciences, China

Dr. Xufeng Yao works as a professor, doctoral supervisor, vice dean of the school of medical imaging, Shanghai University of Medicine and Health Sciences, China. Once, he received his bachelor in medical imaging from Shandong First Medical University, and graduated from Fudan University for his PhD in biomedical engineering, and was a post-doctoral student in optical engineering at University of Shanghai for Science and Technology, China. He was also appointed as a permanent member of the committee of life electronics branch of China institute of electronics society. His current research interest focuses on artificial intelligence in imaging and omics for medicine. Recently, he has published more than 40 academic papers and reviewed for some SCI journals and international conferences. Till now, he has trained above 20 graduate students.
Workshop 6:
Research on Reservoir Computing and Time-Serial Prediction
Summary: Reservoir computing, as a new paradigm of brain like computing, is a low training cost, low hardware overhead recurrent neural network, included three parts: input layer, reservoir layer, and output layer. Reservoir computing breaks through the limitations of traditional neural networks, achieving low-cost and efficient pattern recognition and information processing. It can better adapt to complex and changing environments, and has stronger fault tolerance and robustness. In addition, research on reservoir computing also helps to promote the development of brain like computing and neuromorphic computing, providing new ideas and methods for technological innovation in fields, such as artificial intelligence, the Internet of Things, and human-computer interaction. Although current research is still in a stage of continuous development and improvement, reservoir computing has shown broad application prospects and potential in many fields, such as time series prediction, chaotic sequence prediction, dynamic mode classification, robot control, target tracking, and motion target detection. We hope that researchers can conduct (not limited to) research on reservoir algorithms, reservoir algorithms used for dynamical system prediction and analysis, and the foundation of dynamical systems for reservoir algorithms, and provide theoretical analysis and applications.
 
Keywords: Reservoir Computing; Dynamical Systems;Time-Serial Prediction

                                                                                                                               
Chair: Hui Zhao
University of Jinan, China

Hui Zhao received the Ph.D. degree in Beijing University of Posts and Telecommunications, Beijing, China, in 2017. Since 2017, she has worked at the University of Jinan. Her current research interests include the stability and synchronization of complex dynamical networks (including general complex network, neural network and memristive nueral network), the multi-agent collaborative control, the application of reservoir computing etc.. She published more than 50 academic papers including almost 40 papers indexed by SCI. She has hosted 2 national and provincial projects, such as National Natural Science Foundation of China and Shandong Natural Science Foundation. Currently, she served as the Young Editor of the journal Brain-X, the Guest Associate Editor of the journal Frontiers in Neurobotics, Review Editor of Frontiers in Control Engineering etc.; She served as a member of the Technical Procedures Committee for the 2023 IEEE International Conference on Memristive Computing and Applications (ICMCA), the workshop chair at the 3rd International Conference on Image, Vision and Intelligent Systems in 2023, the Chair of the Organizing Committee of the First Computer Vision Conference in Shandong Province, and reviewer of multiple international journals etc..
Workshop 7:
Deep Learning for Intelligent Scene Perception
Summary: Visual understanding and multi-modality representation fusion are essential to intelligent scene perception. With the rapid progress in machine learning technologies, there are tons of remarkable advances in intelligent scene understanding, whose performance and application fields are extended greatly. However, the complexity of scene could be a challenge for efficient perception. For some application as automatic drive, pedestrian re-identification and robot tracking, the performance and efficiency are typically affected by disturbances in the natural scene. How to efficiently combine information from visual and other modalities to enhance the robustness of perception systems under accidental perturbation and complexity issues is crucial and meaningful. This workshop aims to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of intelligent scene perception. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.
 
Keywords: Computer Vision, Multi-modality Representation Learning, Intelligent Scene Perception and Application, Person re-identification, Deep Learning.

                                                                                                                               
Chair: Zhigang Liu
Northeast Petroleum University, China

Zhigang Liu, received a Ph.D. degree in Computer Resources and Information Engineering from Northeast Petroleum University, and was a visiting scholar with the Department of Electrical & Computer Engineering at National University of Singapore from 2018 to 2019. As a senior CCF member, he is currently the vice president of school of computer and information technology, Northeast Petroleum University. His research interests include machine learning, computer vision, especially, data/label- and computation-efficient deep learning for visual recognition. He participated in the National Natural Science Foundation, Natural Science Foundation of Heilongjiang Province, Scientific and Technological Projects of Petro-China, and Youth Science Foundation of Northeast Petroleum University. Based on these projects, he published many academic papers.