Diverse Knowledge Distillation for End-to-End Person Search
This work provides an incremental improvement in accuracy for end-to-end person search systems, benefiting applications requiring efficient person localization and identification.
This paper addresses the accuracy gap between end-to-end and two-step person search methods, attributing it to the Re-ID sub-networks in end-to-end approaches. The authors propose a new end-to-end network with diverse knowledge distillation and spatial-invariant augmentation, achieving accuracy on par with state-of-the-art two-step methods on CUHK-SYSU and PRW datasets, while maintaining higher efficiency.
Person search aims to localize and identify a specific person from a gallery of images. Recent methods can be categorized into two groups, i.e., two-step and end-to-end approaches. The former views person search as two independent tasks and achieves dominant results using separately trained person detection and re-identification (Re-ID) models. The latter performs person search in an end-to-end fashion. Although the end-to-end approaches yield higher inference efficiency, they largely lag behind those two-step counterparts in terms of accuracy. In this paper, we argue that the gap between the two kinds of methods is mainly caused by the Re-ID sub-networks of end-to-end methods. To this end, we propose a simple yet strong end-to-end network with diverse knowledge distillation to break the bottleneck. We also design a spatial-invariant augmentation to assist model to be invariant to inaccurate detection results. Experimental results on the CUHK-SYSU and PRW datasets demonstrate the superiority of our method against existing approaches -- it achieves on par accuracy with state-of-the-art two-step methods while maintaining high efficiency due to the single joint model. Code is available at: https://git.io/DKD-PersonSearch.