Visual Sensation and Perception Computational Models for Deep Learning: State of the art, Challenges and Prospects
It addresses the need for systematic insights into visual perception models for researchers in AI and cognitive science, but is incremental as a survey.
This paper surveys computational models for visual sensation and perception in deep learning, analyzing biological mechanisms and computational theories to provide a comprehensive reference for future research.
Visual sensation and perception refers to the process of sensing, organizing, identifying, and interpreting visual information in environmental awareness and understanding. Computational models inspired by visual perception have the characteristics of complexity and diversity, as they come from many subjects such as cognition science, information science, and artificial intelligence. In this paper, visual perception computational models oriented deep learning are investigated from the biological visual mechanism and computational vision theory systematically. Then, some points of view about the prospects of the visual perception computational models are presented. Finally, this paper also summarizes the current challenges of visual perception and predicts its future development trends. Through this survey, it will provide a comprehensive reference for research in this direction.