摘要：An intelligent urban system relies on different types of electronic and/or sensor technologies to collect data to facilitate education, security, healthcare, etc. Person reidentification (re-ID) plays a crucial role in intelligent security, in which significant progress has been made during the past few years. One example is using re-ID systems for law enforcement tasks such as suspect identification. One common obstacle is quickly deploying a re-ID system to new city, such as data label deficiency. For example, the lacking of enough labeled data to train an excellent model in a new city, only relying on a tiny amount of criminals’ pictures provided by witnesses. Fortunately, this can be modeled as a special application in unsupervised person re-ID of real-world scenarios, the study of which has become more prevalent in the re-ID community in recent years. In this paper, we first formulate our scenario as a cross-domain few-shot problem and discuss the difference between conventional supervised re-ID and unsupervised re-ID. Then, we introduce a reweighting instance method based on influence function (ReWIF) to guide the training procedure of the re-ID model. This method is motivated by the influence function, and we use two-step optimization to avoid the computation of Hessian matrices. We evaluate our proposed method on public datasets, including Market, Duke, and CUHK. Extensive experimental results show that our method can effectively address the domain bias of different datasets and the absence of labeled data on the target dataset, achieving state-of-the-art performance.
