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7 March 2024 A comparison between deep-learning models for scene recognition
Zongzhen Liu, Jianlin Zhang, Xiaoming Peng
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Proceedings Volume 13086, MIPPR 2023: Pattern Recognition and Computer Vision; 130860C (2024) https://doi.org/10.1117/12.2692330
Event: Twelfth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2023), 2023, Wuhan, China
Abstract
Scene recognition has a wide range of applications in autonomous driving, security monitoring, smart home, and so on. Though traditional methods achieved good results in this field, nowadays deep-learning methods are the dominator. In this paper, we conducted an extensive comparison of the performance of five deep-learning models on a common dataset to reveal their strength and weakness. Experimental results show that, of the five deep learning models, ConvNeXt works the best. In addition, all five models outperform a traditional method that used to be the state of the art.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Zongzhen Liu, Jianlin Zhang, and Xiaoming Peng "A comparison between deep-learning models for scene recognition", Proc. SPIE 13086, MIPPR 2023: Pattern Recognition and Computer Vision, 130860C (7 March 2024); https://doi.org/10.1117/12.2692330
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KEYWORDS
Deep learning
Feature extraction
Pattern recognition
Visual process modeling
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