Abstract Person search aims to locate the target person matching a given query from a list of unconstrained whole images. It is a challenging task due to the unavailable bounding boxes of pedestrians, limited samples for each labeled identity and large amount of unlabeled persons in existing datasets. To address these issues, we propose a novel end-to-end learning framework for person search. The proposed framework settles pedestrian detection and person re-identification concurrently. To achieve the goal of co-learning and utilize the information of unlabeled persons, a novel yet extremely efficient Dynamic Imposter based Online Instance Matching (DI-OIM) loss is formulated. The DI-OIM loss is inspired by the observation that pedestrians appearing in the same image obviously have different identities. Thus we assign the unlabeled persons with dynamic pseudo-labels. The pseudo-labeled persons along with the labeled persons can be used to learn powerful feature representations. Experiments on CUHK-SYSU and PRW datasets demonstrate that our method outperforms other state-of-the-art algorithms. Moreover, it is superior and efficient in terms of memory capacity comparing with existing methods.

中文翻译：

---

基于动态冒名顶替者的在线实例匹配进行人物搜索

Abstract 人物搜索旨在从无约束的完整图像列表中定位与给定查询匹配的目标人物。由于行人的边界框不可用，每个标记身份的样本有限以及现有数据集中大量未标记的人，这是一项具有挑战性的任务。为了解决这些问题，我们提出了一种新颖的端到端的人员搜索学习框架。所提出的框架同时解决了行人检测和人员重新识别问题。为了实现共同学习和利用未标记人员的信息的目标，制定了一种新颖但极其有效的基于动态冒名顶替者的在线实例匹配（DI-OIM）损失。DI-OIM 损失的灵感来自于观察到出现在同一图像中的行人显然具有不同的身份。因此，我们为未标记的人分配动态伪标签。伪标记人与标记人一起可用于学习强大的特征表示。在 CUHK-SYSU 和 PRW 数据集上的实验表明，我们的方法优于其他最先进的算法。此外，与现有方法相比，它在存储容量方面更加优越和高效。