# Multi-Attention-Guided Cascading Network for End-to-End Person Search
## 4 Experiment
### Ablation Study  
In this section, we investigate the effectiveness of various components incorporated in MGCN. The experimental results on CUHK-SYSU and PRW datasets are shown in Table 3. We tested the impact of the alignment module and multiple attention modules on our algorithm. Furthermore, we have check-marked the corresponding modules in Table 3. \"Aligning Module\" denotes that the aligning module is incorporated into the re-id module of the network, enhancing the network's ability to capture the pedestrian details while avoiding background interference. \"Channel Attention\" denotes the re-id feature extracted network with channel module, which acquires the significant channel information in the local region of the person. Moreover, the \"Spatial Attention\" refers to a branch in the network that utilizes a spatial attention module to capture fine-grained part information from the local regions of the person images. \"Aligning Module and Channel Attention\" represents that the spatial attention and aligning module are added on the basis of the re-id network, which integrates the semantic regions-based features and channel features to obtain better results for person search task. \"Channel Attention and Spatial Attention\" means the our proposed model is trained by double attention modules. \"Aligning, Channel Attention and Spatial Attention Module\" means person search network, which includes fine-grained kinds of features extracted by three modules.  
It is clear from Table 3 that the performance of the baseline model is inferior to that of all other models on all datasets. Specifically, the baseline model achieves the lowest mAP on the PRW dataset, while our proposed MGCN model leads to significant improvement in mAP from 46.83% to 47.11%. In addition, we observed similar improvements in the CUHK-SYSU dataset. Compared with the attention module, adding an alignment module in the network can significantly improve the experimental results. This demonstrates that for people with complex backgrounds, the module can effectively suppress redundant information to obtain significant semantic information about individuals.These findings confirm the effectiveness of MGCN through evaluating the performance of its different components. However, when combining the \"Channel attention\" and \"Spatial attention\" modules, the network does not exhibit significant improvement, indicating that solely capturing the details of the person without considering the current scenario information is limited.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Aligning** & **Channel** & **Spatial** & \multicolumn{2}{c}{**CUHK-SYSU**} & \multicolumn{2}{c}{**PRW**} \\
**Module** & **Attention** & **Attention** & **mAP** & **top-1** & **mAP** & **top-1** \\ \hline ✓ & & & 94.24 & 95.03 & 46.83 & 87.07 \\ ✓ & ✓ & & 93.97 & 94.38 & 45.51 & 82.84 \\ ✓ & & ✓ & 94.23 & 95.00 & 44.06 & 86.49 \\  & ✓ & ✓ & 94.01 & 94.62 & 44.12 & 81.67 \\ ✓ & ✓ & ✓ & 94.28 & 94.97 & 47.11 & 86.39 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experimental results on CUHK-SYSU and PRW. The mAP (%) and top-1 are listed.  
Visualization  
We visualize the search results in challenging datasets to verify the effectiveness of MGCN. On the left side are the query images, and on the right side are the gallery images showing pedestrians with correct matching. Each green bounding box is marked with the probability of query, while the highest is the correct pedestrian. In Figure 6, our method shows the ability to correctly identify the same person among multiple interfering factors, which demonstrates the robustness and high discrimination of the features extracted by MGCN.