Nearest-Neighbor (NN) classification has been proven as a simple and effective approach for few-shot learning. The query data can be classified efficiently by finding the nearest support class based on features extracted by pretrained deep models. However, NN-based methods are sensitive to the data distribution and may produce false prediction if the samples in the support set happen to lie around the distribution boundary of different classes. To solve this issue, we present P3DC-Shot, an improved nearest-neighbor based few-shot classification method empowered by prior-driven data calibration. Inspired by the distribution calibration technique which utilizes the distribution or statistics of the base classes to calibrate the data for few-shot tasks, we propose a novel discrete data calibration operation which is more suitable for NN-based few-shot classification. Specifically, we treat the prototypes representing each base class as priors and calibrate each support data based on its similarity to different base prototypes. Then, we perform NN classification using these discretely calibrated support data. Results from extensive experiments on various datasets show our efficient non-learning based method can outperform or at least comparable to SOTA methods which need additional learning steps.

P3DC-Shot是一种基于最近邻居分类的few-shot分类方法，通过离线提取特征来对查询数据进行分类。它可以通过离线提取的特征，找到最接近的支持类并对查询数据进行分类。该方法利用了基础类的分布或统计信息来校准数据，将NN分类使用离散校准支持数据，使得结果在不需要额外学习步骤的情况下可以超越当前最先进的few-shot learning方法的结果。

P3DC-Shot: 基于先验的离散数据校正用于最近邻少样本分类