Agriculture-Vision Challenge 2024 -- The Runner-Up Solution for Agricultural Pattern Recognition via Class Balancing and Model Ensemble
<|reference_start|>Agriculture-Vision Challenge 2024 -- The Runner-Up Solution for Agricultural Pattern Recognition via Class Balancing and Model Ensemble: The Agriculture-Vision Challenge at CVPR 2024 aims at leveraging semantic segmentation models to produce pixel level semantic segmentation labels within regions of interest for multi-modality satellite images. It is one of the most famous and competitive challenges for global researchers to break the boundary between computer vision and agriculture sectors. However, there is a serious class imbalance problem in the agriculture-vision dataset, which hinders the semantic segmentation performance. To solve this problem, firstly, we propose a mosaic data augmentation with a rare class sampling strategy to enrich long-tail class samples. Secondly, we employ an adaptive class weight scheme to suppress the contribution of the common classes while increasing the ones of rare classes. Thirdly, we propose a probability post-process to increase the predicted value of the rare classes. Our methodology achieved a mean Intersection over Union (mIoU) score of 0.547 on the test set, securing second place in this challenge.<|reference_end|>
@article{liu2024agriculture-vision,
    title={Agriculture-Vision Challenge 2024 -- The Runner-Up Solution for
  Agricultural Pattern Recognition via Class Balancing and Model Ensemble},
    author={Wang Liu and Zhiyu Wang and Puhong Duan and Xudong Kang and Shutao Li},
    journal={arXiv preprint arXiv:2406.12271},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12271},
    primaryClass={cs.CV}
}