# Multi-Attention-Guided Cascading Network for End-to-End Person Search
## 2 Related Works
### Person Search  
Person search tasks aim to identify a specific pedestrian according to the given images or videos, which usually contain two steps with person detection and person re-identification task. Compared with person re-id, person search is a more challenging task, which requires detection and distinguishing people in complex environments. In particular, researchers constructed two large-scale datasets, CUHK-SYSU [18] and PRW [16] to verify the performance of the person search algorithms. Therefore, [PERSON] et al. [32] presented a framework for refining the localization of persons based on re-id, which can obtain more reliable bounding boxes and provide more discriminative feature embedding for downstream re-id tasks. [PERSON] et al. [33] introduced a task-consistent two-stage framework for person search, which design an identity-guided detector module to produce query-like bounding boxes for the re-id stage. [PERSON] et al. [34] suggested a novel re-id method with remodeling foreground person and original image patches, which can explore more representative features and improve model performance. Although the two-stage method has obtained great results, it is still unsatisfactory in terms of network computing cost and time.  
In contrast, an end-to-end one-step person search framework combines detection and re-id task in a unified model, which focuses on constructing an effective network to obtain search result without extra steps. [PERSON] et al. [35] trained relational context-aware agents to locate the target person from the scene image, which takes into account key information, such as local visual information, and temporal context. Recently, [PERSON] et al. [36] proposed a Feature-Aligned Person Search Network (AlignPS), which designs a feature-aligned network to solve the misalignment issues and follows the person re-id priority principle to extract more discriminative feature embedding. [PERSON] et al. [37] took the relationship between detection and re-id into consideration to design a sequential network to progress learning each sub-tasks. In particular, they designed the context bipartite graphs matching algorithm to mine context information from different people. As the transformer network developed, some researchers have proposed the unified framework with a transformer module for person search task. [PERSON] et al. [38] proposed a person search-specialized module with transformers for person detection and re-ID, which integrates detection encoder-decoder and re-id decoder to explore the relationship between different parts from the target person. [PERSON] et al. [39] introduced the end-to-end cascade occluded attention transformer framework to solve the scale, perspective and occlusion problems, respectively. Compared to pedestrian detection, which aims to learn the global features of pedestrians, person re-identification requires more emphasis on the fine-grained details and unique characteristics of each individual. Therefore, we should take multiple fine-grained semantic information for each person into consideration in a one-step framework to mine more discriminative feature representation from complex scenes.