Improving the performance of semi-supervised person Re-identification by selecting reliable unlabeled samples.

In this paper, we focus on the semi-supervised person re-identification (Re-ID) task. Under the semi-supervised setting, only a subset of individuals in large-scale datasets need to be labeled for training to reduce the manual annotation cost. To enhance recognition performance under this setting, we propose a novel three-stage, two-branch semi-supervised Re-ID framework. Within this framework, we implement a progressive training process that integrates three methods for training unlabeled data: identification learning based on the pseudo-label estimation technique, metric learning based on mining positive or negative pairwise relationships between samples, and feature consistency learning between views generated through various data augmentations. To make full use of the unlabeled data, the above three methods are gradually added in each training stage, and the defects of these methods are avoided. Moreover, to further improve the reliability of training samples, we design two modules for selecting reliable samples to determine which method to adopt for each unlabeled sample to reduce model performance deterioration due to incorrect pseudo-labels or incorrect sample pairs. Our framework can be combined with existing supervised methods and has less performance cost. Moreover, different components in our proposed framework can be used as plug-ins to integrate into existing semi-supervised Re-ID methods and improve their performance. Extensive experiments were conducted on two public Re-ID benchmarks that demonstrate the effectiveness of our proposed framework. [ABSTRACT FROM AUTHOR]