In recent years, person re-identification (re-ID) has achieved relatively good performance, benefiting from the revival of deep neural networks. However, due to the existence of domain bias which refers to the different data distributions between two domains, it remains challenging to directly deploy a model trained on a labeled source domain to a target domain only with unlabeled data available. In this paper, a Self-Training with Progressive Representation Enhancement (PREST) framework, which comprises a multi-scale self-training method and a view-invariant representation learning module, is proposed to promote re-ID performance on the target domain in an unsupervised manner. More specifically, multi-scale representations, including the global body and local parts of pedestrian images, are utilized to obtain pseudo-labels. Then, some images are selected according to the pseudo-labels to create a new dataset for supervising the fine-tuning process, which is operated iteratively to progressively promote the performance. Furthermore, to mitigate the influence of different styles among sub-domains, in cases where a single sub-domain is captured by one camera, a classifier with a gradient reverse layer is first employed to learn view-invariant representation for pedestrian images with the same identity taken by different cameras; this can further enhance the reliability of the predicted labels and improve the cross-domain re-ID performance. Extensive experiments on three large-scale re-ID datasets demonstrate that our framework achieves significantly better performance than existing approaches.

中文翻译：

---

用于无监督跨域人员重新识别的渐进式表示增强的自我训练


近年来，行人重识别（re-ID）取得了相对较好的性能，受益于深度神经网络的复兴。然而，由于域偏差的存在（指两个域之间的不同数据分布），仅在未标记数据可用的情况下将在标记源域上训练的模型直接部署到目标域仍然具有挑战性。本文提出了一种渐进式表示增强自训练（PREST）框架，该框架包括多尺度自训练方法和视图不变表示学习模块，旨在提高目标域上的 re-ID 性能。无监督的方式。更具体地说，利用多尺度表示（包括行人图像的全局身体和局部部分）来获得伪标签。然后，根据伪标签选择一些图像来创建新的数据集来监督微调过程，迭代操作以逐步提高性能。此外，为了减轻子域之间不同风格的影响，在一个摄像机捕获单个子域的情况下，首先采用具有梯度反向层的分类器来学习具有相同特征的行人图像的视图不变表示。不同摄像头拍摄的身份信息；这可以进一步增强预测标签的可靠性并提高跨域重识别性能。对三个大规模 re-ID 数据集的广泛实验表明，我们的框架比现有方法实现了显着更好的性能。