State-of-the-art unsupervised re-ID methods train the neural networks using a memory-based non-parametric softmax loss. Instance feature vectors stored in memory are assigned pseudo-labels by clustering and updated at instance level. However, the varying cluster sizes leads to inconsistency in the updating progress of each cluster. To solve this problem, we present Cluster Contrast which stores feature vectors and computes contrast loss at the cluster level. Our approach employs a unique cluster representation to describe each cluster, resulting in a cluster-level memory dictionary. In this way, the consistency of clustering can be effectively maintained throughout the pipline and the GPU memory consumption can be significantly reduced. Thus, our method can solve the problem of cluster inconsistency and be applicable to larger data sets. In addition, we adopt different clustering algorithms to demonstrate the robustness and generalization of our framework. The application of Cluster Contrast to a standard unsupervised re-ID pipeline achieves considerable improvements of 9.9%, 8.3%, 12.1% compared to state-of-the-art purely unsupervised re-ID methods and 5.5%, 4.8%, 4.4% mAP compared to the state-of-the-art unsupervised domain adaptation re-ID methods on the Market, Duke, and MSMT17 datasets. Code is available at https://github.com/alibaba/cluster-contrast.

翻译：当前最先进的无监督行人重识别方法采用基于记忆的非参数化softmax损失训练神经网络。存储在记忆中的实例特征向量通过聚类分配伪标签，并以实例级进行更新。然而，聚类的规模差异会导致各簇更新进度不一致。为解决此问题，我们提出簇对比学习（Cluster Contrast），该方法在簇级存储特征向量并计算对比损失。本方法采用独特的簇表征来描述每个簇，从而构建簇级记忆字典。通过这种方式，可以在整个流程中有效维持聚类一致性，并显著降低GPU内存消耗。因此，我们的方法能够解决聚类不一致问题，并适用于更大规模的数据集。此外，我们采用不同聚类算法来验证所提框架的鲁棒性与泛化能力。将簇对比学习应用于标准无监督行人重识别流程，在Market、Duke和MSMT17数据集上分别比最先进的纯无监督重识别方法提升9.9%、8.3%、12.1%的mAP，比最先进的无监督域适应重识别方法提升5.5%、4.8%、4.4%的mAP。代码已开源至https://github.com/alibaba/cluster-contrast。