RepCFT: A Fusion Model of Reparameterized Convolutional Neural Network and Visual Transformer for Wood Species Recognition-Reference-Cited by-同舟云学术

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RepCFT: A Fusion Model of Reparameterized Convolutional Neural Network and Visual Transformer for Wood Species Recognition

Published:2023-11-17 Issue: Volume:35 Page:343-349
ISSN:
Container-title:Proceedings of the 4th International Conference on Artificial Intelligence and Computer Engineering
language:
Short-container-title:

Author:

Wang Junyan¹^ORCID,Lian Ziyan²^ORCID,Kang Feiyang³^ORCID,Chen Jinkai⁴^ORCID

Affiliation:

1. School of Internet, Anhui University, China

2. School of Software Engineering, University of Science and Technology of China, China

3. School of Advanced Technology, University of Science and Technology of China, China

4. Department of Network management center, China Mobile Communications Group Anhui Co., LTD, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3652628.3652685

Reference19 articles.

1. A brief review of machine vision in the context of automated wood identification systems

2. Sharif Razavian, A., Azizpour, H., Sullivan, J., & Carlsson, S. 2014. CNN features off-the-shelf: an astounding baseline for recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 806-813).

3. Identification of North American softwoods via machine-learning

4. Cognitive spectroscopy for wood species identification: near infrared hyperspectral imaging combined with convolutional neural networks

5. Multi-Fusion Approach for Wood Microscopic Images Identification Based on Deep Transfer Learning

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