  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.
