论文标题
语义引导的聚类和半监督者重新识别的深度渐进学习
Semantics-Guided Clustering with Deep Progressive Learning for Semi-Supervised Person Re-identification
论文作者
论文摘要
人重新识别（RE-ID）需要一个人在相机视图中匹配同一个人的图像。作为一项更具挑战性的任务，半监督的重新ID解决了一个问题，即训练数据中只有许多身份被完全标记，而其余的则没有标记。假设这样的标记和未标记的培训数据共享了不相交的身份标签，我们提出了一个具有深度渐进学习（SGC-DPL）语义引导聚类的新颖框架，以共同利用上述数据。通过推进所提出的语义引导的亲和力传播（SG-AP），我们能够根据标记的语义指导，以渐进的方式将伪标记分配给选定的未标记数据。结果，我们的方法能够在半监督环境中增强标记的培训数据。我们对两个大型人重新ID基准测试的实验证明了我们SGC-DPL在不同程度的监督方面的优越性。总体而言，我们的SGC-DPL的概括能力在其他任务中也得到了诸如车辆重新ID或图像检索的其他任务，并通过半监督设置进行了验证。
Person re-identification (re-ID) requires one to match images of the same person across camera views. As a more challenging task, semi-supervised re-ID tackles the problem that only a number of identities in training data are fully labeled, while the remaining are unlabeled. Assuming that such labeled and unlabeled training data share disjoint identity labels, we propose a novel framework of Semantics-Guided Clustering with Deep Progressive Learning (SGC-DPL) to jointly exploit the above data. By advancing the proposed Semantics-Guided Affinity Propagation (SG-AP), we are able to assign pseudo-labels to selected unlabeled data in a progressive fashion, under the semantics guidance from the labeled ones. As a result, our approach is able to augment the labeled training data in the semi-supervised setting. Our experiments on two large-scale person re-ID benchmarks demonstrate the superiority of our SGC-DPL over state-of-the-art methods across different degrees of supervision. In extension, the generalization ability of our SGC-DPL is also verified in other tasks like vehicle re-ID or image retrieval with the semi-supervised setting.
