Leveraging Virtual and Real Person for Unsupervised Person Re-Identification

Person re-identification (re-ID) is a challenging instance retrieval problem, especially when identity annotations are not available for training. Although modern deep re-ID approaches have achieved great improvement, it is still difficult to optimize the deep re-ID model and learn discriminative person representation without annotations in training data. To address this challenge, this study considers the problem of unsupervised person re-ID and introduces a novel approach to solve this problem by leveraging virtual and real data. Our approach includes two components: virtual person generation and training of the deep re-ID model . For virtual person generation, we learn a person generation model and a camera style transfer model using unlabeled real data to generate virtual persons with different poses and camera styles. The virtual data is formed as labeled training data, enabling subsequent training deep re-ID model in supervision. For training of the deep re-ID model, we divide it into three steps: 1) pre-training a coarse re-ID model by using virtual data; 2) collaborative filtering based positive pair mining from the real data; and 3) fine-tuning of the coarse re-ID model by leveraging the mined positive pairs and virtual data. The final re-ID model is achieved by iterating between step 2 and step 3 until convergence. Extensive experiments demonstrate the effectiveness of our method. Experimental results on two large-scale datasets, Market-1501 and DukeMTMC-reID, show the advantages of our method over state-of-the-art approaches in unsupervised person re-ID. Our code is now available online. 1