Imitating targets from all sides: an unsupervised transfer learning method for person re-identification

Abstract

Person re-identification (Re-ID) models usually present a limited performance when they are trained on one dataset and tested on another dataset due to the inter-dataset bias (e.g. completely different identities and backgrounds) and the intra-dataset difference (e.g. camera and pose changes). In other words, the absence of identity labels (who the person is) and pairwise labels (whether a pair of images belongs to the same person or not) leads to failures in unsupervised person Re-ID problem. We argue that synchronous consideration of these two aspects can improve the performance of unsupervised person Re-ID model. In this work, we introduce a Classification and Latent Commonality (CLC) method based on transfer learning for the unsupervised person Re-ID problem. Our method has three characteristics: (1) proposing an imitate model to generate an imitated target domain with estimated identity labels and create a pseudo target domain to compensate the pairwise labels across camera views; (2) formulating a dual classification loss on both the source domain and imitated target domain to learn a discriminative representation and diminish the inter-domain bias; (3) investigating latent commonality and reducing the intra-domain difference by constraining triplet loss on the source domain, imitated target domain and pairwise label target domain (composed of pseudo target domain and target domain). Extensive experiments are conducted on three widely employed benchmarks, including Market-1501, DukeMTMC-reID and MSMT17, and experimental results demonstrate that the proposed method can achieve a competitive performance against other state-of-the-art unsupervised Re-ID approaches.