  Person search aims to localize specific a target person from a gallery set of
images with various scenes. As the scene of moving pedestrian changes, the
captured person image inevitably bring in lots of background noise and
foreground noise on the person feature, which are completely unrelated to the
person identity, leading to severe performance degeneration. To address this
issue, we present a Scene-Adaptive Person Search (SEAS) model by introducing
bilateral modulations to simultaneously eliminate scene noise and maintain a
consistent person representation to adapt to various scenes. In SEAS, a
Background Modulation Network (BMN) is designed to encode the feature extracted
from the detected bounding box into a multi-granularity embedding, which
reduces the input of background noise from multiple levels with norm-aware.
Additionally, to mitigate the effect of foreground noise on the person feature,
SEAS introduces a Foreground Modulation Network (FMN) to compute the clutter
reduction offset for the person embedding based on the feature map of the scene
image. By bilateral modulations on both background and foreground within an
end-to-end manner, SEAS obtains consistent feature representations without
scene noise. SEAS can achieve state-of-the-art (SOTA) performance on two
benchmark datasets, CUHK-SYSU with 97.1\% mAP and PRW with 60.5\% mAP. The code
is available at https://github.com/whbdmu/SEAS.
