  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.
