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Computational knowledge vision: paradigmatic knowledge based prescriptive learning and reasoning for perception and vision

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Abstract

This paper outlines a novel advanced framework that combines structurized knowledge and visual models—Computational Knowledge Vision. In advanced studies of image and visual perception, a visual model’s understanding and reasoning ability often determines whether it works well in complex scenarios. This paper presents the state-of-the-art mainstream of vision models for visual perception. This paper then proposes a concept and basic framework of Computational Knowledge Vision that extends the knowledge engineering methodology to the computer vision field. In this paper, we first retrospect prior work related to Computational Knowledge Vision in the light of the connectionist and symbolist streams. We discuss neural network models, meta-learning models, graph models, and Transformer models in detail. We then illustrate a basic framework for Computational Knowledge Vision, whose essential techniques include structurized knowledge, knowledge projection, and conditional feedback. The goal of the framework is to enable visual models to gain the ability of representation, understanding, and reasoning. We also describe in-depth works in Computational Knowledge Vision and its extensions in other fields.

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Acknowledgements

This work is supported in part by National Key R&D Program of China (2020YFB1600400), in part by Key Research and Development Program of Guangzhou (202007050002), in part by National Natural Science Foundation of China (61806198, U1811463), and in part by the National Key R&D Program of China (2018AAA0101502).

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Authors and Affiliations

  1. School of Computer and Artificial Intelligence, Wuhan University of Technology, Wuhan, China

    Wenbo Zheng

  2. The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Lan Yan & Fei-Yue Wang

  3. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China

    Lan Yan & Fei-Yue Wang

  4. School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, China

    Chao Gou

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  1. Wenbo Zheng
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  2. Lan Yan
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  3. Chao Gou
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  4. Fei-Yue Wang
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Contributions

WZ: Conceptualization, Methodology, Software, Validation, Investigation, Writing—Original Draft, Writing - Review & Editing;  LY: Methodology, Investigation, Writing—Original Draft;  CG: Conceptualization, Resources, Writing—Review & Editing, Project administration, Funding acquisition;  FW Resources, Writing—Review & Editing, Supervision, Funding acquisition.

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Correspondence to Fei-Yue Wang.

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Zheng, W., Yan, L., Gou, C. et al. Computational knowledge vision: paradigmatic knowledge based prescriptive learning and reasoning for perception and vision. Artif Intell Rev 55, 5917–5952 (2022). https://doi.org/10.1007/s10462-022-10166-9

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