论文标题
通过多标签分类的无监督人员重新识别
Unsupervised Person Re-identification via Multi-label Classification
论文作者
论文摘要
无监督的人重新识别（REID）的挑战在于学习歧视性特征而没有真正的标签。本文将无监督的人REID作为多标签分类任务，以逐步寻求真正的标签。我们的方法首先使用单级标签分配每个人的图像，然后通过利用更新的REID模型进行标签预测来演变为多标签分类。标签预测包括相似性计算和循环一致性，以确保预测标签的质量。为了提高多标签分类中的REID模型训练效率，我们进一步提出了基于内存的多标签分类损失（MMCL）。 MMCL与基于内存的非参数分类器一起工作，并在统一框架中集成了多标签分类和单标签分类。我们的标签预测和MMCL在迭代和大大提高了REID的性能。对几个大型人REID数据集进行的实验证明了我们方法在无监督的人Reid中的优越性。我们的方法还允许在其他域中使用标记的人图像。在此转移学习设置下，我们的方法还实现了最先进的性能。
The challenge of unsupervised person re-identification (ReID) lies in learning discriminative features without true labels. This paper formulates unsupervised person ReID as a multi-label classification task to progressively seek true labels. Our method starts by assigning each person image with a single-class label, then evolves to multi-label classification by leveraging the updated ReID model for label prediction. The label prediction comprises similarity computation and cycle consistency to ensure the quality of predicted labels. To boost the ReID model training efficiency in multi-label classification, we further propose the memory-based multi-label classification loss (MMCL). MMCL works with memory-based non-parametric classifier and integrates multi-label classification and single-label classification in a unified framework. Our label prediction and MMCL work iteratively and substantially boost the ReID performance. Experiments on several large-scale person ReID datasets demonstrate the superiority of our method in unsupervised person ReID. Our method also allows to use labeled person images in other domains. Under this transfer learning setting, our method also achieves state-of-the-art performance.
