LAUSR

Abstract Most of the existing person re-identification algorithms rely on supervised model learning from a large number of labeled training data per-camera-pair. However, the manual annotations often require expensive human labor, which limits the application of supervised methods for large-scale real-world deployments. To address this problem, we formulate a Neighbor Similarity and Soft-label Adaptation (NSSA) algorithm to transfer the supervised information from source domain to a new unlabeled target dataset. Specifically, we introduce a distance metric on the target domain, which incorporates inner-domain neighbor similarity and inter-domain soft-label adapted from source domain. The unlabeled samples which are close in this metric are considered to share the same pseudo-id and further selected to fine-tune the model. The training is performed iteratively to incorporate more credible sample pairs and incrementally improve the model. Extensive experimental results validate the superiority of our proposed NESSA algorithm, which significantly outperforms the state-of-the-art unsupervised and domain adaptation re-identification methods.