解决农业作物分类中真实数据分布不均问题，提升小样本模型泛化能力。
Mind the Gap: Bridging Prior Shift in Realistic Few-Shot Crop-Type Classification
- 用狄利克雷分布模拟真实世界标签分布，动态增强训练数据先验
- 在少样本场景下显著改善决策边界，提升模型稳定性
- 适合处理长尾分布的农业图像分类任务，尤其适用于标注稀缺场景
现实农业数据常呈现严重的类别不平衡，通常符合长尾分布。作物类型分类的标注数据本就稀少且获取成本高。在小样本学习中，训练集常被人为平衡，无法反映真实场景，导致训练与测试标签分布存在偏差，降低模型在真实环境中的泛化能力。为此，我们提出狄利克雷先验增强（DirPA），在模型训练过程中主动模拟目标域未知的标签分布偏移。具体地，将真实分布建模为狄利克雷分布的随机变量，实现小样本学习中的先验增强。实验表明，DirPA能有效调整决策边界，并通过作为动态特征正则项稳定训练过程。
原文摘要 · Abstract (English)
Real-world agricultural distributions often suffer from severe class imbalance, typically following a long-tailed distribution. Labeled datasets for crop-type classification are inherently scarce and remain costly to obtain. When working with such limited data, training sets are frequently constructed to be artificially balanced -- in particular in the case of few-shot learning -- failing to reflect real-world conditions. This mismatch induces a shift between training and test label distributions, degrading real-world generalization. To address this, we propose Dirichlet Prior Augmentation (DirPA), a novel method that simulates an unknown label distribution skew of the target domain proactively during model training. Specifically, we model the real-world distribution as Dirichlet-distributed random variables, effectively performing a prior augmentation during few-shot learning. Our experiments show that DirPA successfully shifts the decision boundary and stabilizes the training process by acting as a dynamic feature regularizer.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。