论文标题
端到端人搜索的各种知识蒸馏
Diverse Knowledge Distillation for End-to-End Person Search
论文作者
论文摘要
人搜索旨在从图像画廊中定位和识别特定人员。最近的方法可以分为两组，即两步和端到端的方法。以前的视图人搜索是两个独立的任务，并使用单独训练的人检测和重新识别（RE-ID）模型实现了主导的结果。后者以端到端的方式进行人搜索。尽管端到端方法提高了推理效率，但就准确性而言，它们在很大程度上落后于那两步的效率。在本文中，我们认为两种方法之间的差距主要是由端到端方法的重新ID子网络引起的。为此，我们提出了一个简单而强大的端到端网络，并具有多样化的知识蒸馏以打破瓶颈。我们还设计了空间不变的增强，以帮助模型以不准确的检测结果不变。 CUHK-SYSU和PRW数据集的实验结果证明了我们方法与现有方法的优越性 - 它以最先进的两步方法的准确性达到了准确性，同时由于单个关节模型而保持了高效率。代码可在：https：//git.io/dkd-personsearch中找到。
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.
