  The goal of person search is to localize and match query persons from scene
images. For high efficiency, one-step methods have been developed to jointly
handle the pedestrian detection and identification sub-tasks using a single
network. There are two major challenges in the current one-step approaches. One
is the mutual interference between the optimization objectives of multiple
sub-tasks. The other is the sub-optimal identification feature learning caused
by small batch size when end-to-end training. To overcome these problems, we
propose a decoupled and memory-reinforced network (DMRNet). Specifically, to
reconcile the conflicts of multiple objectives, we simplify the standard
tightly coupled pipelines and establish a deeply decoupled multi-task learning
framework. Further, we build a memory-reinforced mechanism to boost the
identification feature learning. By queuing the identification features of
recently accessed instances into a memory bank, the mechanism augments the
similarity pair construction for pairwise metric learning. For better encoding
consistency of the stored features, a slow-moving average of the network is
applied for extracting these features. In this way, the dual networks reinforce
each other and converge to robust solution states. Experimentally, the proposed
method obtains 93.2% and 46.9% mAP on CUHK-SYSU and PRW datasets, which exceeds
all the existing one-step methods.
