In recent years, supervised person re-identification (re-ID) models have received increasing studies. However, these models trained on the source domain always suffer dramatic performance drop when tested on an unseen domain. Existing methods are primary to use pseudo labels to alleviate this problem. One of the most successful approaches predicts neighbors of each unlabeled image and then uses them to train the model. Although the predicted neighbors are credible, they always miss some hard positive samples, which may hinder the model from discovering important discriminative information of the unlabeled domain. In this paper, to complement these low recall neighbor pseudo labels, we propose a joint learning framework to learn better feature embeddings via high precision neighbor pseudo labels and high recall group pseudo labels. The group pseudo labels are generated by transitively merging neighbors of different samples into a group to achieve higher recall. However, the merging operation may cause subgroups in the group due to imperfect neighbor predictions. To utilize these group pseudo labels properly, we propose using a similarity-aggregating loss to mitigate the influence of these subgroups by pulling the input sample towards the most similar embeddings. Extensive experiments on three large-scale datasets demonstrate that our method can achieve state-of-the-art performance under the unsupervised domain adaptation re-ID setting.

中文翻译：

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用于人员重新识别的无监督域适应的补充伪标签

近年来，受监管人员重新识别（re-ID）模型已得到越来越多的研究。但是，当在看不见的域上进行测试时，这些在源域上训练的模型始终会遭受严重的性能下降。现有方法是使用伪标签缓解此问题的主要方法。最成功的方法之一是预测每个未标记图像的邻居，然后使用它们来训练模型。尽管预测的邻居是可信的，但它们始终会错过一些困难的正样本，这可能会阻止模型发现未标记域的重要区分信息。在本文中，为了补充这些低召回率邻居伪标签，我们提出了一个联合学习框架，以通过高精度邻居伪标签和高召回率组伪标签学习更好的特征嵌入。通过将不同样本的邻居过渡合并到一个组中以实现更高的召回率，可以生成组伪标签。但是，由于不完善的邻居预测，合并操作可能会导致组中的子组。为了正确利用这些组伪标签，我们建议使用相似度聚集损失，通过将输入样本拉向最相似的嵌入来减轻这些子组的影响。在三个大型数据集上进行的大量实验表明，我们的方法可以在无人监督的域自适应re-ID设置下达到最新的性能。为了正确利用这些组伪标签，我们建议使用相似度聚集损失，通过将输入样本拉向最相似的嵌入来减轻这些子组的影响。在三个大型数据集上进行的大量实验表明，我们的方法可以在无人监督的域自适应re-ID设置下达到最新的性能。为了正确利用这些组伪标签，我们建议使用相似度聚集损失，通过将输入样本拉向最相似的嵌入来减轻这些子组的影响。在三个大型数据集上进行的大量实验表明，我们的方法可以在无人监督的域自适应re-ID设置下达到最新的性能。