Deep Semi-Supervised Person Re-Identification With External Memory

To overcome the scalability problem of supervised person re-identification (Re-ID), we consider the semi-supervised person Re-ID problem of learning from a limited number of labeled images of a few identities and a large number of unlabeled images. To this end, we propose an external-memory-based deep semi-supervised person Re-ID model (EDS). Based on the external memory, two loss functions are designed so as to effectively cope with the relation between labeled and unlabeled data for overcoming the limitation of batch size in each epoch in deep learning. Therefore, an effective deep semi-supervised learning method can be performed. Extensive experiments validate the superiority of the proposed method for semi-supervised person Re-ID.