Unsupervised domain adaptive reidentification: Theory and practice Abstract   We study the problem of unsupervised domain adaptive reidentification (reID) which is an active topic in computer vision but lacks a theoretical foundation. We first extend existing unsupervised domain adaptive classification theories to reID tasks. Concretely, we introduce some assumptions on the extracted feature space and then derive several loss functions guided by these assumptions. To optimize them, a novel selftraining scheme for unsupervised domain adaptive reID tasks is proposed. It iteratively makes guesses for unlabeled target data based on an encoder and trains the encoder based on the guessed labels. Extensive experiments on unsupervised domain adaptive person reID and vehicle reID tasks with comparisons to the stateofthearts confirm the effectiveness of the proposed theories and selftraining framework. Our code is available on  https:github.comLcDogDomainAdaptiveReID .

DomainCamera Adaptation for Unsupervised Person ReIdentification Although supervised person reidentification (ReID) performance has been significantly improved in recent years, it is still a challenge for unsupervised person ReIddue to its absence of labels across disjoint camera views. On the other hand, ReIdmodels trained on source domain usually offer poor performance when they are tested on target domain due to interdomain bias e.g. different classes and intradomain difference e.g camera variance. To overcome this problem, given a labeled source training domain and an unlabeled target training domain, we propose an unsupervised transfer method, DomainCamera Adaptation model, to generate a pseudo target domain by bridging interdomain bias and intradomain difference. The idea is to fill the absence of labels in target domain by transferring labeled images of source domain to target domain across cameras. Then we propose a crossdomain classification loss to extract discriminative representation across domains. The intuition is to think of unsupervised learning as semisupervised learning in target domain. We evaluate our deep model on Market1501 and DukeMTMCreID and the results show our model outperforms the stateofart unsupervised ReID methods by large margins.