Distilled Person Re-Identification: Towards a More Scalable System

Abstract

Person re-identification (Re-ID), for matching pedestrians across non-overlapping camera views, has made great progress in supervised learning with abundant labelled data. However, the scalability problem is the bottleneck for applications in large-scale systems. We consider the scalability problem of Re-ID from three aspects: (1) low labelling cost by reducing label amount, (2) low extension cost by reusing existing knowledge and (3) low testing computation cost by using lightweight models. The requirements render scalable Re-ID a challenging problem. To solve these problems in a unified system, we propose a Multi-teacher Adaptive Similarity Distillation Framework, which requires only a few labelled identities of target domain to transfer knowledge from multiple teacher models to a user-specified lightweight student model without accessing source domain data. We propose the Log-Euclidean Similarity Distillation Loss for Re-ID and further integrate the Adaptive Knowledge Aggregator to select effective teacher models to transfer target-adaptive knowledge. Extensive evaluations show that our method can extend with high scalability and the performance is comparable to the state-of-the-art unsupervised and semi-supervised Re-ID methods.