Learning a Domain-Invariant Embedding for Unsupervised Person Re-identification

Person re-identification (Re-ID) aims at matching images of the same person where images are captured by non-overlapping camera views distributed at different locations. To solve this problem, most recent works require a large pre-labeled dataset for training a deep model. These methods are not always suitable for real-world applications, because the latter often lack labeled data. In order to tackle this drawback, we proposed a novel Domain-Invariant Embedding Network (DIEN) to learn a domain-invariant embedding (DIE) feature by introducing a multi-loss joint learning with Recurrent Top- Down Attention (RTDA) mechanism. Due to the improvement in traditional triplet loss, our proposed model can benefit from both source-domain (labeled) data and target-domain (unlabeled) data. Furthermore, the resulting DIE feature not only has improved class discrimination but also robustness to domain shift. We compared our method with recent competitive algorithms and also evaluated the effectiveness of the proposed modules.