Multiscale Deep Learning for Detection and Recognition: A Comprehensive Survey.
IEEE Trans Neural Netw Learn Syst
; PP2024 Apr 23.
Article
en En
| MEDLINE
| ID: mdl-38652624
ABSTRACT
Recently, the multiscale problem in computer vision has gradually attracted people's attention. This article focuses on multiscale representation for object detection and recognition, comprehensively introduces the development of multiscale deep learning, and constructs an easy-to-understand, but powerful knowledge structure. First, we give the definition of scale, explain the multiscale mechanism of human vision, and then lead to the multiscale problem discussed in computer vision. Second, advanced multiscale representation methods are introduced, including pyramid representation, scale-space representation, and multiscale geometric representation. Third, the theory of multiscale deep learning is presented, which mainly discusses the multiscale modeling in convolutional neural networks (CNNs) and Vision Transformers (ViTs). Fourth, we compare the performance of multiple multiscale methods on different tasks, illustrating the effectiveness of different multiscale structural designs. Finally, based on the in-depth understanding of the existing methods, we point out several open issues and future directions for multiscale deep learning.
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1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
IEEE Trans Neural Netw Learn Syst
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Estados Unidos