Focus More on What? Guiding Multi-Task Training for End-to-End Person Search

End-to-end person search is a research domain that executes the task of locating and identifying a target individual from a large number of scene images via a multi-task framework. However, a major challenge for the learning of end-to-end methods is the inherent conflicts between the two sub-tasks: person detection focuses on identifying generic features of persons, while person re-identification (Re-ID) strives to find unique, distinguishing features for matching the target person. Unlike previous research focusing on model architectures, this paper delves into the end-to-end person search training process. We find that the unbalanced and conflicting training issues significantly impair the learning efficiency of the Re-ID sub-task, which directly influences person search accuracy. To address this, we propose a novel Guiding Multi-Task Training (GMT) framework that facilitates end-to-end balanced learning for person search. We introduce a Guiding Multi-Task Harmonious Learning (GMHL) module, which decouple the features and then performs intra- and cross-task feature interaction to enhance the learning of each sub-task. Moreover, GMT employs a Balancing Multi-Task Oriented Fusing (BMOF) method to explicitly enhance Re-ID sub-task learning through additional Re-ID training and target-guided multi-model parameters fusion. Extensive experiments on 2 benchmark datasets, CUHK-SYSU and PRW, show that GMT achieves leading performance with 96.0% mAP and 61.3% mAP, respectively.