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Updated: Jun 28, 2025

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Multiscale Deep Learning for Detection and Recognition: A Comprehensive Survey.

Licheng Jiao, Mengjiao Wang, Xu Liu

    IEEE Transactions on Neural Networks and Learning Systems
    |April 23, 2024
    PubMed
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    This study explores multiscale representation in computer vision, detailing methods for object detection and recognition. It reviews deep learning approaches, compares their performance, and suggests future research directions for multiscale modeling.

    Area of Science:

    • Computer Vision
    • Deep Learning

    Background:

    • The multiscale problem in computer vision is gaining attention for object detection and recognition.
    • Human vision's multiscale mechanism provides insights into computer vision challenges.

    Purpose of the Study:

    • To comprehensively review and structure knowledge on multiscale representation in deep learning.
    • To introduce advanced multiscale representation methods and their theoretical underpinnings.

    Main Methods:

    • Exploration of pyramid representation, scale-space representation, and multiscale geometric representation.
    • Analysis of multiscale modeling in Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs).

    Main Results:

    • Comparison of the performance of various multiscale methods across different computer vision tasks.

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  • Illustration of the effectiveness of diverse multiscale structural designs.

    • Conclusions:

    • Identification of open issues and future research directions in multiscale deep learning.
    • Provides a foundational understanding for advancing multiscale techniques in computer vision.