Agriculture-Vision Challenge 2022 -- The Runner-Up Solution for Agricultural Pattern Recognition via Transformer-based Models
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Abstract (arXiv)
The Agriculture-Vision Challenge in CVPR is one of the most famous and competitive challenges for global researchers to break the boundary between computer vision and agriculture sectors, aiming at agricultural pattern recognition from aerial images. In this paper, we propose our solution to the third Agriculture-Vision Challenge in CVPR 2022. We leverage a data pre-processing scheme and several Transformer-based models as well as data augmentation techniques to achieve a mIoU of 0.582, accomplishing the 2nd place in this challenge.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 9 references that are in the map. First posted on arXiv 23 Jun 2022.
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5 of 9 references.
References · 5
- 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,940
- 2019Segmentation Transformer: Object-Contextual Representations for Semantic Segmentationcited by 576
- 2018Deep learning in agriculture: A surveycited by 160
- 2020Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysiscited by 61
- 2022Augmentation Invariance and Adaptive Sampling in Semantic Segmentation of Agricultural Aerial Imagescited by 8
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