Previous works propose the distance‐based sampling for unlabeled datapoints to address the few‐shot person re‐identification task, however, many selected samples may be assigned with wrong labels due to poor feature quality in these works, which negatively affects the learning procedure. In this article, we propose a novel sampling strategy to improve the quality of assigned pseudo‐labels, thus promoting the final performance. To illustrate, we first propose the concept of variance confidence to measure the credibility of pseudo‐labels, then we apply a novel variance subsampling algorithm to improve the accuracy of the selected sample labels. Our method combines distance confidence and variance confidence as a two‐round sampling criterion. Meanwhile, a variation decay strategy is used in our sampling process in combination with the actual distribution of features. We evaluate our approach on two publicly available datasets, MARS and DukeMTMC‐VideoReID, and achieve state‐of‐the‐art one‐shot performance.

中文翻译：

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基于方差子采样算法的单次视频行人重识别

以前的工作提出了对未标记数据点的基于距离的采样来解决少样本人重新识别任务，但是，由于这些工作中的特征质量差，许多选定的样本可能被分配了错误的标签，这对学习过程产生了负面影响。在本文中，我们提出了一种新颖的采样策略来提高分配伪标签的质量，从而提高最终性能。为了说明这一点，我们首先提出方差置信度的概念来衡量伪标签的可信度，然后我们应用一种新颖的方差子采样算法来提高所选样本标签的准确性。我们的方法结合了距离置信度和方差置信度作为两轮抽样标准。同时，在我们的采样过程中结合了特征的实际分布使用了变异衰减策略。我们在两个公开可用的数据集 MARS 和 DukeMTMC-VideoReID 上评估我们的方法，并实现了最先进的一次性性能。