Computational Knowledge Vision (eBook)

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

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.

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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