Unsupervised person re-identification (ReID) aims at learning discriminative identity features for person retrieval without any annotations. Recent advances accomplish this task by leveraging clustering-based pseudo labels, but these pseudo labels are inevitably noisy which deteriorate model performance. In this paper, we propose a Neighbour Consistency guided Pseudo Label Refinement (NCPLR) framework, which can be regarded as a transductive form of label propagation under the assumption that the prediction of each example should be similar to its nearest neighbours'. Specifically, the refined label for each training instance can be obtained by the original clustering result and a weighted ensemble of its neighbours' predictions, with weights determined according to their similarities in the feature space. In addition, we consider the clustering-based unsupervised person ReID as a label-noise learning problem. Then, we proposed an explicit neighbour consistency regularization to reduce model susceptibility to over-fitting while improving the training stability. The NCPLR method is simple yet effective, and can be seamlessly integrated into existing clustering-based unsupervised algorithms. Extensive experimental results on five ReID datasets demonstrate the effectiveness of the proposed method, and showing superior performance to state-of-the-art methods by a large margin.
翻译:无人监督的人重新身份识别(ReID)旨在学习个人检索的歧视性身份特征(ReID) 。最近的进展通过利用基于集群的假标签来完成这项任务,但这些假标签不可避免地噪音,使模型性能恶化。在本文件中,我们提议一个以邻居为主的以聚合为主的引导 Pseedo Label Refinement (NCPLR) 框架,这个框架可以被视为标签传播的转导形式,假设对每个示例的预测应当与最近的邻居相似。具体地说,每个实例的改良标签可以通过原始集群结果和邻国的加权组合预测组合得到,其加权组合的组合的预测加在一起,其重量根据功能空间的相似性确定。此外,我们认为基于集群的无人监督的人重新ID(ReID)是一个标签性学习问题。然后,我们提出明确的邻居一致性规范,以减少模型在改进培训稳定性的同时过度适应性。NCPLR方法既简单又有效,并且可以与现有的基于集群的、不超强的算法相连接地整合。在五个ReID数据模型上的广泛实验性结果展示了高水平的绩效。