解决农业图像分类中类别不平衡问题，提升小样本模型泛化能力
DirPA: Addressing Prior Shift in Imbalanced Few-shot Crop-type Classification
- 引入狄利克雷先验增强方法，主动模拟真实分布偏移
- 在欧盟多国数据上验证，显著提升极端长尾分布下的分类准确率
- 适合关注农业视觉识别与小样本学习的科研及应用人员
现实农业监测常面临严重类别不平衡和高标签获取成本，导致数据极度稀缺。在为数据稀缺场景设计的小样本学习（FSL）框架中，训练集通常被人为平衡，但这与自然界中常见的长尾分布不符，引发分布偏移，削弱模型在真实农业任务中的泛化能力。我们此前提出狄利克雷先验增强（DirPA；Reuss et al., 2026a），以在训练阶段主动缓解标签分布偏移的影响。本文将该方法扩展至欧盟多个成员国，超越局部实验，检验其在多样农业环境下的鲁棒性。结果表明，DirPA在不同地理区域均有效：不仅提升了系统鲁棒性并稳定了训练过程，且在极端长尾分布下显著改善了各类别特异性性能，通过主动模拟先验实现。
原文摘要 · Abstract (English)
Real-world agricultural monitoring is often hampered by severe class imbalance and high label acquisition costs, resulting in significant data scarcity. In few-shot learning (FSL) -- a framework specifically designed for data-scarce settings -- , training sets are often artificially balanced. However, this creates a disconnect from the long-tailed distributions observed in nature, leading to a distribution shift that undermines the model's ability to generalize to real-world agricultural tasks. We previously introduced Dirichlet Prior Augmentation (DirPA; Reuss et al., 2026a) to proactively mitigate the effects of such label distribution skews during model training. In this work, we extend the original study's geographical scope. Specifically, we evaluate this extended approach across multiple countries in the European Union (EU), moving beyond localized experiments to test the method's resilience across diverse agricultural environments. Our results demonstrate the effectiveness of DirPA across different geographical regions. We show that DirPA not only improves system robustness and stabilizes training under extreme long-tailed distributions, regardless of the target region, but also substantially improves individual class-specific performance by proactively simulating priors.
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