Person search is a challenging task that involves detecting and retrieving
individuals from a large set of un-cropped scene images. Existing person search
applications are mostly trained and deployed in the same-origin scenarios.
However, collecting and annotating training samples for each scene is often
difficult due to the limitation of resources and the labor cost. Moreover,
large-scale intra-domain data for training are generally not legally available
for common developers, due to the regulation of privacy and public security.
Leveraging easily accessible large-scale User Generated Video Contents
(\emph{i.e.} UGC videos) to train person search models can fit the open-world
distribution, but still suffering a performance gap from the domain difference
to surveillance scenes. In this work, we explore enhancing the out-of-domain
generalization capabilities of person search models, and propose a
generalizable framework on both feature-level and data-level generalization to
facilitate downstream tasks in arbitrary scenarios. Specifically, we focus on
learning domain-invariant representations for both detection and ReID by
introducing a multi-task prototype-based domain-specific batch normalization,
and a channel-wise ID-relevant feature decorrelation strategy. We also identify
and address typical sources of noise in open-world training frames, including
inaccurate bounding boxes, the omission of identity labels, and the absence of
cross-camera data. Our framework achieves promising performance on two
challenging person search benchmarks without using any human annotation or
samples from the target domain.

我们探索了增强人物搜索模型的域外泛化能力，并提出了一个可推广的框架，通过引入基于多任务原型的领域特定批量归一化和通道相关的特征去相关策略来促进任意场景中的下游任务。