Exploring vision transformer: classifying electron-microscopy pollen images with transformer
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-022-07789-y.pdf
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4. Cubuk ED, Zoph B, Mane D, Vasudevan V, Le QV (2019) Autoaugment: learning augmentation strategies from data. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 113–123
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Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. SwinT-SRNet: Swin transformer with image super-resolution reconstruction network for pollen images classification;Engineering Applications of Artificial Intelligence;2024-07
2. Advancing electron microscopy using deep learning;Journal of Physics: Materials;2024-02-08
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