  Person search aims at localizing and identifying a query person from a
gallery of uncropped scene images. Different from person re-identification
(re-ID), its performance also depends on the localization accuracy of a
pedestrian detector. The state-of-the-art methods train the detector
individually, and the detected bounding boxes may be sub-optimal for the
following re-ID task. To alleviate this issue, we propose a re-ID driven
localization refinement framework for providing the refined detection boxes for
person search. Specifically, we develop a differentiable ROI transform layer to
effectively transform the bounding boxes from the original images. Thus, the
box coordinates can be supervised by the re-ID training other than the original
detection task. With this supervision, the detector can generate more reliable
bounding boxes, and the downstream re-ID model can produce more discriminative
embeddings based on the refined person localizations. Extensive experimental
results on the widely used benchmarks demonstrate that our proposed method
performs favorably against the state-of-the-art person search methods.
