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 deteriorates 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 from the original clustering result and a weighted ensemble of its neighbours’ predictions, with weights determined according to their similarities in the feature space. Furthermore, we also explore building a unified global-local NCPLR mechanism through a global-local label interaction module to achieve mutual label refinement. Such a strategy promotes efficient complementary learning while mitigating some unreliable information, finally improving the quality of the refined pseudo labels for each global-local region. Extensive experimental results demonstrate the effectiveness of the proposed method, showing superior performance to state-of-the-art methods by a large margin. Our source code is released in https://github.com/haichuntai/NCPLR-ReID .

中文翻译：

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邻居一致性和全局-局部交互：一种用于无监督人员重新识别的新型伪标签细化方法


无监督人员重新识别 （ReID） 旨在学习判别性身份特征，以便在没有任何注释的情况下进行人员检索。最近的进展通过利用基于聚类的伪标签来完成这项任务，但这些伪标签不可避免地会产生噪声，这会降低模型性能。在本文中，我们提出了一个邻域一致性指导的伪标签细化 （NCPLR） 框架，该框架可以被视为标签传播的一种转导形式，假设每个样本的预测应与其最近的邻居相似。具体来说，每个训练实例的优化标签可以从原始聚类结果及其相邻预测的加权集成中获得，权重根据它们在特征空间中的相似性确定。此外，我们还探索通过全局-本地标签交互模块构建统一的全局-本地 NCPLR 机制，以实现标签相互细化。这样的策略促进了高效的互补学习，同时减少了一些不可靠的信息，最终提高了每个全球-局部区域的精炼伪标签的质量。广泛的实验结果证明了所提出的方法的有效性，显示出比最先进的方法更优越的性能。我们的源代码在 https://github.com/haichuntai/NCPLR-ReID 年发布。