  Person search aims to localize and identify a specific person from a gallery
of images. Recent methods can be categorized into two groups, i.e., two-step
and end-to-end approaches. The former views person search as two independent
tasks and achieves dominant results using separately trained person detection
and re-identification (Re-ID) models. The latter performs person search in an
end-to-end fashion. Although the end-to-end approaches yield higher inference
efficiency, they largely lag behind those two-step counterparts in terms of
accuracy. In this paper, we argue that the gap between the two kinds of methods
is mainly caused by the Re-ID sub-networks of end-to-end methods. To this end,
we propose a simple yet strong end-to-end network with diverse knowledge
distillation to break the bottleneck. We also design a spatial-invariant
augmentation to assist model to be invariant to inaccurate detection results.
Experimental results on the CUHK-SYSU and PRW datasets demonstrate the
superiority of our method against existing approaches -- it achieves on par
accuracy with state-of-the-art two-step methods while maintaining high
efficiency due to the single joint model. Code is available at:
https://git.io/DKD-PersonSearch.
