Neural Person Search Machines
This work addresses the challenge of efficiently and accurately finding specific individuals in unconstrained images, which is incremental by building on existing person search methods with a novel recursive approach.
The paper tackles the problem of person search in the wild by proposing a recursive localization method that shrinks the search area from the whole image to precisely locate the target person, achieving state-of-the-art performance on benchmark datasets with improvements in mAP and top-1 metrics.
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.