In this article, we propose a novel method to simultaneously solve the data problem of dirty quality and poor quantity for person reidentification (ReID). Dirty quality refers to the wrong labels in image annotations. Poor quantity means that some identities have very few images (FewIDs). Training with these mislabeled data or FewIDs with triplet loss will lead to low generalization performance. To solve the label error problem, we propose a weighted label correction based on cross-entropy (wLCCE) strategy. Specifically, according to the influence range of the wrong labels, we first classify the mislabeled images into point label error and set label error. Then, we propose a weighted triplet loss (WTL) to correct the two label errors, respectively. To alleviate the poor quantity issue, we propose a feature simulation based on autoencoder (FSAE) method to generate some virtual samples for FewID. For the authenticity of the simulated features, we transfer the difference pattern of identities with multiple images (MultIDs) to FewIDs by training an autoencoder (AE)-based simulator. In this way, the FewIDs obtain richer expressions to distinguish from other identities. By dealing with a dirty and poor data problem, we can learn more robust ReID models using the triplet loss. We conduct extensive experiments on two public person ReID datasets: 1) Market-1501 and 2) DukeMTMC-reID, to verify the effectiveness of our approach.

中文翻译：

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PRDP：使用脏数据和不良数据进行人员重新识别


在本文中，我们提出了一种新方法来同时解决行人重新识别（ReID）的质量脏和数量少的数据问题。脏质量是指图像注释中的错误标签。数量少意味着某些身份的图像很少（FewID）。使用这些错误标记的数据或具有三元组损失的 FewID 进行训练将导致泛化性能较低。为了解决标签错误问题，我们提出了一种基于交叉熵（wLCCE）策略的加权标签校正。具体来说，根据错误标签的影响范围，我们首先将错误标签图像分为点标签误差和集合标签误差。然后，我们提出加权三元组损失（WTL）来分别纠正两个标签错误。为了缓解数量不足的问题，我们提出了一种基于自动编码器（FSAE）的特征模拟方法来为FewID生成一些虚拟样本。为了保证模拟特征的真实性，我们通过训练基于自动编码器（AE）的模拟器，将多个图像（MultID）的身份差异模式转移到FewID。这样，FewID就获得了更丰富的表达方式来区别于其他身份。通过处理脏数据和不良数据问题，我们可以使用三元组损失学习更稳健的 ReID 模型。我们对两个公众 ReID 数据集进行了广泛的实验：1）Market-1501 和 2）DukeMTMC-reID，以验证我们方法的有效性。