In this work, we propose to address the unsupervised domain adaptive (UDA) person re-id problem in which the model learns from an unlabeled target domain using a fully annotated source domain. Current approaches mainly address domain shift problem or the inter/intra-domain variation of the two domains. However, they have neglected to integrate the-easy-to-learn label distribution of the target domain into the model to improve its performance. Moreover, the automatic label assignment for the unlabeled target data currently used in UDA methods does not reflect the underlying data. To address these issues, we introduce a technique that enforces three properties: (1) target instance invariance that considers the target data and uses a key–value memory to guess the label distribution that is later used as the supervision signal. (2) a camera invariance, formed by unlabeled target images, and their camera-style transferred. Here, a new loss function is proposed to control overconfident predictions on the styled images. Lastly, (3) a hierarchical clustering-based optimization technique that exploits the similarities between the target images to constrain the supervision information of the first property. Here, we randomly allocate each target image to a separate cluster, then gradually incorporate similarity within each identity as we group similar images into clusters and use the cluster-IDs as the new target labels. We iteratively refine the guessed label distribution of the target domain by making predictions on the unlabeled target domain and then train the network with these new samples. Extensive experimental results on the concurrent use of these three properties demonstrate that the proposed model can achieve the state-of-the-art on unsupervised domain adaptive person re-id. Our work is important for knowledge discovery and knowledge transfer.

在这项工作中，我们建议解决无监督域自适应（UDA）人员re-id问题，其中模型使用完全注释的源域从未标记的目标域中学习。当前的方法主要解决域移位问题或两个域的域间/域内变化。但是，他们忽略了将目标域的易于学习的标签分布集成到模型中以提高其性能。而且，UDA方法中当前使用的未标记目标数据的自动标签分配不会反映基础数据。为了解决这些问题，我们引入了一种技术，该技术具有三个属性：（1）目标实例不变性，它考虑目标数据并使用键值存储器猜测标签分布，该标签分布随后用作监督信号。（2）相机不变性，由未标记的目标图像组成，并转移了其相机样式。在此，提出了一种新的损失函数来控制样式图像上的过度自信的预测。最后，（3）一种基于层次聚类的优化技术，该技术利用目标图像之间的相似性来约束第一属性的监管信息。在这里，我们将每个目标图像随机分配到一个单独的群集中，然后在将相似图像分组到群集中并使用群集ID作为新的目标标签时，将相似性逐渐纳入每个标识中。我们通过对未标记目标域进行预测来迭代地优化目标域的推测标签分布，然后使用这些新样本训练网络。关于这三个属性的并发使用的大量实验结果表明，所提出的模型可以在无人监督的领域自适应人re-id上达到最新水平。我们的工作对于知识发现和知识转移非常重要。