We investigate the problem of person search in the wild in this work. Instead of comparing the query against all candidate regions generated in a query-blind manner, we propose to recursively shrink the search area from the whole image till achieving precise localization of the target person, by fully exploiting information from the query and contextual cues in every recursive search step. We develop the Neural Person Search Machines (NPSM) to implement such recursive localization for person search. Benefiting from its neural search mechanism, NPSM is able to selectively shrink its focus from a loose region to a tighter one containing the target automatically. In this process, NPSM employs an internal primitive memory component to memorize the query representation which modulates the attention and augments its robustness to other distracting regions. Evaluations on two benchmark datasets, CUHK-SYSU Person Search dataset and PRW dataset, have demonstrated that our method can outperform current state-of-the-arts in both mAP and top-1 evaluation protocols.

本文提出了一种基于神经网络的人物搜索方法，通过递归式地缩小搜索区域并利用查询信息和上下文线索实现对目标人物的精确定位；其中使用内部记忆体组件来记忆查询表示，以增强对其他干扰区域的鲁棒性。在两个标准数据集上的实验表明，该方法在 mAP 和 top-1 评估协议方面均优于目前的最新技术水平。

神经人员搜索机