Person re-identification (ReID) aims to match pedestrian images across disjoint cameras. Existing supervised ReID methods utilize deep networks and train them with identity-labeled images, which suffer from limited annotations. Recently, clustering-based unsupervised ReID attracts more and more attention. It first clusters unlabeled images and assigns cluster index to the pseudo-identity-labels, then trains a ReID model with the pseudo-identity-labels. However, considering the slight inter-class variations and significant intra-class variations, pseudo-identity-labels learned from clustering algorithms are usually noisy and coarse. To alleviate the problems above, besides clustering pseudo-identity-labels, we propose to learn pseudo-patch-labels, which brings two advantages: (1) Patch naturally alleviates the effect of backgrounds, occlusions, and carryings since they usually occupy small parts in images, thus overcome noisy labels. (2) It is plausible that patches from different pedestrians belong to the same pseudo-identity-label. For example, pedestrians have a high probability of wearing either the same shoes or pants but a low possibility of wearing both. The experiments demonstrate our proposed method achieves the best performance by a large margin on both image- and video-based datasets.

中文翻译：

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用于无监督学习人员重新识别的多视图标签预测


行人重新识别 (ReID) 旨在匹配不相交摄像头的行人图像。现有的监督式 ReID 方法利用深度网络并使用带有身份标记的图像对其进行训练，但这些图像的注释有限。近年来，基于聚类的无监督ReID受到越来越多的关注。它首先对未标记的图像进行聚类，并将聚类索引分配给伪身份标签，然后使用伪身份标签训练 ReID 模型。然而，考虑到轻微的类间变化和显着的类内变化，从聚类算法中学习到的伪身份标签通常是有噪声和粗糙的。为了缓解上述问题，除了聚类伪身份标签之外，我们建议学习伪补丁标签，这带来了两个优点：（1）补丁自然地减轻了背景、遮挡和携带的影响，因为它们通常占据很小的部分在图像中，从而克服噪声标签。 (2)来自不同行人的斑块可能属于相同的伪身份标签。例如，行人穿着相同鞋子或裤子的可能性很高，但两者都穿的可能性很低。实验表明，我们提出的方法在基于图像和视频的数据集上都取得了很大的最佳性能。