Attribute-Identity Embedding and Self-Supervised Learning for Scalable Person Re-Identification

Abstract

Due to the domain shift between source dataset and target dataset, most of the existing person re-identification (PRID) algorithms trained by a supervised learning framework often fail to be well generalized to another domain. To address this challenge, we propose a self-supervised learning algorithm based on attribute-identity embedding, which can incrementally optimize the model by selecting unlabeled samples from target domain. Thus the gap between source domain and target domain is bridged. Specifically, we first develop an attribute-identity joint prediction dictionary learning model for simultaneously learning a latent attribute space, a semantic attribute dictionary and an identifier. In our method, the predicted attribute from latent attribute space is used as a bridge to establish a preliminary link between different domains so as to predict the label of the target data sample. Second, to exploit the latent label contained in the predicted samples, we propose a prediction-training cycle self-supervised learning to tune the model variables to make them more adaptive in the target domain. Finally, the similarity measurement of pedestrians is achieved by combining the attribute space with latent identity space. The experiments show that the developed method outperforms some state-of-the-art supervised PRID methods and unsupervised PRID algorithms.