MTLSegFormer: Multi-task Learning with Transformers for Semantic Segmentation in Precision Agriculture-Reference-Cited by-同舟云学术

MTLSegFormer: Multi-task Learning with Transformers for Semantic Segmentation in Precision Agriculture

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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Author:

Goncalves Diogo Nunes¹,Marcato Jose²,Zamboni Pedro²,Pistori Hemerson³,Li Jonathan⁴,Nogueira Keiller⁵,Goncalves Wesley Nunes¹

Affiliation:

1. Federal University of Mato Grosso do Sul,Faculty of Computer Science,MS,Brazil,79070-900

2. Federal University of Mato Grosso do Sul,Faculty of Engineering, Architecture, and Urbanism and Geography,MS,Brazil,79070-900

3. Dom Bosco Catholic University,INOVISAO,MS,Brazil,79117-900

4. University of Waterloo,Department of Geography and Environmental Management,Waterloo,Ontario,Canada,N2L 3G1

5. University of Stirling,Stirling,Scotland,UK,FK9 4LA

Funder

Nvidia

Publisher

IEEE

Link

Reference43 articles.

1. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture;eigen;CoRR,2014

2. Segformer: Simple and efficient design for semantic segmentation with transformers;xie;Neural Information Processing Systems,2021

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