# Multi-Attention-Guided Cascading Network for End-to-End Person Search
###### Abstract
It can be found that both one-step and two-step person search methods need reliable embedded feature representation to achieve accurate retrieval. Above all, person detection tends to extract global features in the scene, and then distinguish the different features between the background and instances, while re-id task needs to distinguish the differences between various similar persons. Therefore, an effective multi-attention end-to-end framework should be introduced for completing person search  
Figure 1: Person search: detection a target person from whole scene images. tasks, which is competent to extract multiple noteworthy features based on the valuable information collected from multiple people. To address the aforementioned challenges, we propose a novel Multi-Attention-Guided Cascading Network (MGCN) for end-to-end person search task, which integrates multiple attention information from the target person in a unified one-step network to achieve fine-grained person search tasks. Inspired by the Faster R-CNN network, MGCN adaptively labeled the trustworthy bounding box for each person in various scenes. Furthermore, we adopt a multi-attention-guided network to directly obtain feature embedding representations for the re-id task, which takes both channel attention and spatial attention into consideration to obtain fine-grained information from each person. Moreover, MGCN adopt aligning module to focus on region from the labeled person with more discriminative information to construct interference of background information. Finally, in the test stage, we utilize bipartite graph processing to explore the similarity between various people in the search results to obtain optimal matching results. The entire MGCN process is illustrated in detail in Figure 2.  
* \"In this paper, we propose a novel method termed Multi-Attention-Guided Cascading Network (MGCN), which can integrate multiple discriminative information with coarse-to-fine features from people to be retrieved for end-to-end person search tasks. By this unified framework, the trusted bounding box and person discriminant information can be exploited effectively\". * \"Moreover, we explore the attention maps of different clues to extract the person's semantic regions, which promotes the network attention to different salient information from pedestrians. In addition, in order to reduce the interference of background information in the scene, we design the alignment module in our end-to-end network\". * \"Experiments on two challenging datasets confirm that MGCN markedly outperforms other person search methods and the proposed multi-attention module has significant advantages\". Figure 2: The flow chart of a Multi-Attention-Guided Cascading Network (MGCN), which integrates multi-attention discriminative information from a person in an end-to-end network for person search task. MGCN first mark multiple person by detection network, which provides a high-quality bounding box for each person to obtain label information. Then, MGCN utilize aligning module to locate advanced semantic information from the input image and avoid interference caused by background information. Finally, we introduce a multi-attention module to promote network focus on distinguishing fine-grained information between the multiple person for the re-id task. We organized this paper as follows. In Section 1, we introduced the development process of person search and some typical algorithms. Meanwhile, Section 2 provided the related works for person re-id and search task. Section 3 introduces MGCN network and corresponding loss function in detail. To demonstrate the superiority of our approach, we conducted person search experiments in Section 4. A detailed conclusion of this paper is presented in Section 5.