Person search is a challenging computer vision task that handles and optimizes both pedestrian detection and person re-identification simultaneously. Person search is also closer to real-world applications compared to person re-identification. Existing person search works mainly focused on refining loss functions, using more complex network structures or redefining the person search as another task. However, few of them attempted to solve this problem from a feature representation perspective. In this paper, we embark on this point and present a novel method called FLAG to learn a better feature representation for person search. Specifically, partition pooling and cross-level feature hybridization are proposed to guide the model to learn more discriminative person features. Experiments show that the proposed method achieves encouraging performance improvement and outperforms similar end-to-end person search methods.

中文翻译：

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FLAG：特征学习以及人物搜索的额外指导

人员搜索是一项具有挑战性的计算机视觉任务，可同时处理和优化行人检测和人员重新识别。与人员重新识别相比，人员搜索也更接近于现实世界的应用程序。现有的人员搜索工作主要集中在改进损失函数、使用更复杂的网络结构或将人员搜索重新定义为另一项任务。然而，很少有人试图从特征表示的角度解决这个问题。在本文中，我们从这一点出发，提出了一种称为 FLAG 的新方法来学习更好的人物搜索特征表示。具体而言，提出了分区池化和跨级特征混合，以指导模型学习更具判别力的人物特征。