Swap Path Network for Robust Person Search Pre-training

In person search, we detect and rank matches to a query person image within a set of gallery scenes. Most person search models make use of a feature extraction back-bone, followed by separate heads for detection and re-identification. While pretraining methods for vision back-bones are well-established, pretraining additional modules for the person search task has not been previously exam-ined. In this work, we present the first framework for end-to-end person search pretraining. Our framework splits person search into object-centric and query-centric methodolo-gies, and we show that the query-centric framing is robust to label noise, and trainable using only weakly-labeled person bounding boxes. Further, we provide a novel model dubbed Swap Path Net (SPNet) which implements both query-centric and object-centric training objectives, and can swap between the two while using the same weights. Using SPNet, we show that query-centric pretraining, followed by object-centric fine-tuning, achieves state-of-the-art results on the standard PRW and CUHK-SYSU person search benchmarks, with 96.4% mAP on CUHK-SYSU and 61.2% mAP on PRW. In addition, we show that our method is more effective, efficient, and robust for person search pretraining than recent backbone-only pretraining alter-natives.