GPS: Generalizable Person Search on Large-Scale User-Generated Video Content

Person search is a challenging task that involves detecting and retrieving individuals from a large set of un-cropped scene images. Existing person search models are mostly trained and deployed in the same-origin scenarios. However, collecting and annotating training samples for each scene is difficult due to the limitation of resources and 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 (i.e. UGC videos) to train person search models can fit the real-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 UGC 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. The code is available at https://github.com/caposerenity/GPS.