On Convolutional Vision Transformers for Yield Prediction
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866913227567267840 |
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| author | Inderka, Alvin Huber, Florian Steinhage, Volker |
| author_facet | Inderka, Alvin Huber, Florian Steinhage, Volker |
| contents | While a variety of methods offer good yield prediction on histogrammed remote sensing data, vision Transformers are only sparsely represented in the literature. The Convolution vision Transformer (CvT) is being tested to evaluate vision Transformers that are currently achieving state-of-the-art results in many other vision tasks. CvT combines some of the advantages of convolution with the advantages of dynamic attention and global context fusion of Transformers. It performs worse than widely tested methods such as XGBoost and CNNs, but shows that Transformers have potential to improve yield prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_05557 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On Convolutional Vision Transformers for Yield Prediction Inderka, Alvin Huber, Florian Steinhage, Volker Computer Vision and Pattern Recognition While a variety of methods offer good yield prediction on histogrammed remote sensing data, vision Transformers are only sparsely represented in the literature. The Convolution vision Transformer (CvT) is being tested to evaluate vision Transformers that are currently achieving state-of-the-art results in many other vision tasks. CvT combines some of the advantages of convolution with the advantages of dynamic attention and global context fusion of Transformers. It performs worse than widely tested methods such as XGBoost and CNNs, but shows that Transformers have potential to improve yield prediction. |
| title | On Convolutional Vision Transformers for Yield Prediction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.05557 |