Semantics-Guided Clustering with Deep Progressive Learning for Semi-Supervised Person Re-identification

• URL: http://arxiv.org/abs/2010.01148v1
• Date: Fri, 2 Oct 2020 18:02:35 GMT
• Title: Semantics-Guided Clustering with Deep Progressive Learning for Semi-Supervised Person Re-identification
• Authors: Chih-Ting Liu, Yu-Jhe Li, Shao-Yi Chien, Yu-Chiang Frank Wang
• Abstract summary: Person re-identification (re-ID) requires one to match images of the same person across camera views. We propose a novel framework of Semantics-Guided Clustering with Deep Progressive Learning (SGC-DPL) to jointly exploit the above data. Our approach is able to augment the labeled training data in the semi-supervised setting.
• Score: 58.01834972099855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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