Deep Unsupervised Progressive Learning for Distant Domain Adaptation The superiority of deeply learned representation has been reported in very recent literature of reidentification (ReID) task. In this paper, we study a novel transfer learning problem termed Distant Domain Transfer Learning (DDTL) for ReID task. Different from existing transfer learning problems which assume that there is a close relation between source domain and target domain, in the DDTL problem, target domain can be totally different from source domain. For example, the source domain classifies pedestrian images but the target domain distinguishes vehicle images. In this work, our goal is to execute an unseen and unrelated task based on a labeled dataset training previously without any samples from intermediate domains. Particularly, we consider the more pragmatic issue of learning a deep feature with no labels, and propose a Deep Unsupervised Progressive Learning (DUPL) method to transfer pretrained deep representations to unseen domains. Specifically, our work performs clustering and finetuning of the CNN to improve the performance of original model trained on the irrelevant labeled dataset. Empirical studies on distant domain adaptation task (pedestrian u003e vehicle) demonstrate the effectiveness of the proposed method, and the improvement in terms of the mAP accuracy is up to 15% over nontransfer methods.

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.