# Multi-Attention-Guided Cascading Network for End-to-End Person Search
## 4 Experiment
### Experiment Results on Two Datasets
#### 4.2.2 Evaluation on PRW  
The comparison of results on the PRW dataset is presented in Tables 1 and 2. Notably, the PRW dataset has a smaller training set compared to other datasets, while the size of the gallery is larger. MGCN achieves better performance than other methods in most cases. The results show that the proposed model achieves high mAP and top-1 accuracy of 47.11% and 86.39%, respectively, on the PRW dataset. Since PRW dataset has fewer training samples, most of the deep learning methods are prone to over-fitting problems in the model training process. Compared with end-to-end method, two-stage algorithms relies more on previous step label information to train the network model, so processing person data in complex scenes is still not robust. This also demonstrates that joint deal with person detection and re-id task in the unified framework can improve the search performance,  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**CUHK-SYSU**} & \multicolumn{2}{c}{**PRW**} \\ \cline{2-5}
**Method** & **mAP** & **top-1** & **mAP** & **top-1** \\ \hline DPM [16] & - & - & 20.50 & 48.30 \\ MGTS [17] & 83.00 & 83.70 & 32.60 & 72.10 \\ CLSA [49] & 87.20 & 88.5 & 38.7 & 65.00 \\ RDLR [32] & 93.00 & 94.20 & 42.90 & 70.20 \\ IGPN [50] & 90.3 & 91.40 & 47.20 & 87.00 \\ TCTS [33] & 93.90 & 95.10 & 46.80 & 87.50 \\ MGCN & **94.28** & **94.97** & **47.11** & **86.39** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results on CUHK-SYSU and PRW. The mAP (%) and top-1 are listed.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**CUHK-SYSU**} & \multicolumn{2}{c}{**PRW**} \\ \cline{2-5}
**Method** & **mAP** & **top-1** & **mAP** & **top-1** \\ \hline OIM [18] & 75.50 & 78.70 & 21.30 & 49.90 \\ IAN [51] & 76.30 & 80.10 & 23.00 & 61.90 \\ NPSM [52] & 77.90 & 81.20 & 24.20 & 53.10 \\ RCAA [35] & 79.30 & 81.30 & - & \\ CTXG [53] & 84.10 & 86.50 & 33.40 & 73.60 \\ QEEPS [19] & 88.90 & 89.10 & 37.10 & 76.70 \\ HOIM [54] & 89.70 & 90.80 & 39.80 & 80.40 \\ APNet [55] & 88.90 & 89.30 & 41.90 & 81.40 \\ BINet [56] & 90.00 & 90.70 & 45.30 & 81.70 \\ NAE [42] & 91.50 & 92.40 & 43.30 & 80.90 \\ NAE+ [42] & 92.10 & 92.90 & 44.00 & 81.10 \\ DMRNet [57] & 93.20 & 94.20 & 46.90 & 83.30 \\ PGS [58] & 92.30 & 94.70 & 44.20 & 85.20 \\ AlignPS [36] & 93.10 & 93.40 & 45.90 & 81.90 \\ AlignPS+ [36] & 94.00 & 94.50 & 46.10 & 82.10 \\ SeqNet [37] & 93.80 & 94.60 & 46.70 & 83.40 \\ MGCN & **94.28** & **94.97** & **47.11** & **86.39** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results on CUHK-SYSU and PRW. The mAP (%) and top-1 are listed.  
which can reduce the error caused by sub-optimal solution. For example, compared to AlignPS+, MGCN shows significant improvements of 1.01% and 4.29% in mAP and top-1, respectively. Furthermore, compared with baseline NAE, our method is higher than NAE by 5.49% in terms of top-1. We attribute this to our multi-attention structure which generates more fine-grained re-id features, especially when there is a complex setting with background information and personnel details are not obvious. MGCN can obtain a reliable regression bounding box in the detection stage and mine more discriminative pedestrian characteristics from the original scene. Moreover, MGCN integrates multiple attention information in the re-id feature extraction module, which can focus on simultaneously channel and spatial significant information to improve search performance.