Computational Knowledge Vision: The First Footprints - Softcover

Zheng, Wenbo; Wang Ph.D, Fei-Yue

Softcover

ISBN 10: 0443216193 ISBN 13: 9780443216190

Publisher: Academic Press, 2024

Synopsis

Computational Knowledge Vision: The First Footprints presents a novel, advanced framework which combines structuralized knowledge and visual models. In advanced image and visual perception studies, a visual model's understanding and reasoning ability often determines whether it works well in complex scenarios. This book presents state-of-the-art mainstream vision models for visual perception. As computer vision is one of the key gateways to artificial intelligence and a significant component of modern intelligent systems, this book delves into computer vision systems that are highly specialized and very limited in their ability to do visual reasoning and causal inference.

Questions naturally arise in this arena, including (1) How can human knowledge be incorporated with visual models? (2) How does human knowledge promote the performance of visual models? To address these problems, this book proposes a new framework for computer vision–computational knowledge vision.

• Presents a concept and basic framework of Computational Knowledge Vision that extends the knowledge engineering methodology to the computer vision field
• Discusses neural networks, meta-learning, graphs, and Transformer models
• Illustrates a basic framework for Computational Knowledge Vision whose essential techniques include structuralized knowledge, knowledge projection, and conditional feedback

About this title

About the Authors

Wenbo Zheng received his bachelor degree in software engineering from Wuhan University of Technology, Wuhan, China, in 2017. He received his Ph.D. degree in computer science and technology from the Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China, in 2021. He is currently an Associate Professor at the School of Computer and Artificial Intelligence, Wuhan University of Technology. His research interests include computer vision and machine learning.

Prof. Fei-Yue Wang is the State Specially Appointed Expert and the Founding Director of the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, in China. He is also a research Professor at the DeSci Center of Parallel Intelligence, Obuda University, Budapest, Hungary. His research focuses on intelligent control, social computing, and knowledge automation, with a particular emphasis on energy and complex systems. He pioneered the concepts of Social Energy and the Parallel Energy initiative, aimed at integrating social intelligence into energy management and optimization. Additionally, his research explores methods and applications for parallel intelligence, social computing, and knowledge automation. He is a Fellow of INCOSE, IFAC, ASME, and AAAS. In 2007, he received the National Prize in Natural Sciences of China, numerous best papers awards from IEEE Transactions, and became an Outstanding Scientist of ACM for his work in intelligent control and social computing. In 2024, he received IEEE CRFID Emily Sopensky Meritorious Service Award, IEEE SMC Lotfi A. Zadeh Pioneer Award, and 2025 IEEE Transportation Technologies Award. In 2025, he received IEEE ITSS Lifetime Achievement Award. Additionally, in 2021, he was selected as the IFAC Pavel J. Nowacki Distinguished Lecturer.

From the Back Cover

Computational Knowledge Vision: The First Footprints presents a novel advanced framework which combines structuralized knowledge and visual models―Computational Knowledge Vision. In advanced image and visual perception studies, a visual model's understanding and reasoning ability often determine whether it works well in complex scenarios. This book presents state-of-the-art mainstream vision models for visual perception.

Computer vision is one of the key gateways to artificial intelligence and a significant component of modern intelligent systems. Currently, computer vision systems are highly specialized and very limited in their ability to do visual reasoning and causal inference. We need task-specific designs and large amounts of labeled training data for model training. However, the models trained in this way still lack a general understanding of common sense that is obvious to the average adult and cannot describe the workings of our physical and social worlds. Humans can thoroughly understand visual scenes because of priori knowledge from many domains and can learn and reason effectively based on their knowledge. Questions naturally arise (1) how can human knowledge be incorporated with visual models? (2) how does human knowledge promote the performance of visual models? To address these problems, Computational Knowledge Vision: The First Footprints proposes a new framework for computer vision– computational knowledge vision.

Edition details

• Publisher: Academic Press
• Publication date: 2024
• Language: English
• ISBN 10: 0443216193
• ISBN 13: 9780443216190
• Binding: Paperback
• Edition number: 1
• Number of pages: 276