# Attention-Aware Adversarial Network for Person Re-Identification
## 4 Experimental Results
### Influence of the Attention Assignment Network  
As elaborated in Section 3.2, the attention map predicted in this stage not only focused on the interesting regions that contributed most to the final results, but also the other important regions that indicated the unique features of the person. In the next stage, the parameters of the attention assignment network were employed to fine-tune the attention-aware adversarial network. As a result,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Rank-1 on BL+ Euclidean**} & \multicolumn{2}{c}{**Rank-1 on BL + XQDA**} & \multicolumn{2}{c}{**Rank-1 on BL + KISSME**} \\ \cline{2-7}  & **Rank-1** & **mAP** & **Rank-1** & **mAP** & **Rank-1** & **mAP** \\ \hline baseline & 77.9 & 54.8 & 78.0 & 56.5 & 78.9 & 55.6 \\ Resnet-second & 79.6 & 55.8 & 79.3 & 57.3 & 80.3 & 56.8 \\
**Resnet-third** & 80.3 & **57.0** & **80.2** & **59.1** & **81.5** & **58.8** \\ Resnet-fourth & **80.6** & **57.0** & 79.8 & 58.6 & 80.7 & 58.1 \\ Resnet-fifth & 79.6 & 56.0 & 78.5 & 56.7 & 79.8 & 56.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of inserting the occlusion operation in different locations of the network. “Baseline” represents the baseline network.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**The Values of \(\lambda_{1}\), \(\lambda_{2}\)**} & \multicolumn{2}{c}{**Rank-1 on BL+ Euclidean**} & \multicolumn{2}{c}{**Rank-1 on BL + XQDA**} & \multicolumn{2}{c}{**Rank-1 on BL + KISSME**} \\ \cline{2-5}  & **Rank-1** & **mAP** & **Rank-1** & **mAP** & **Rank-1** & **mAP** \\ \hline
1, 1 & 69.6 & 50.8 & 71.2 & 52.9 & **72.7** & 53.0 \\
0,5 & 1 & 70.8 & 51.2 & 71.7 & 53.0 & 72.3 & **53.2** \\
1, 1 & 7.0 & 50.4 & 72.1 & 52.8 & 72.1 & 52.6 \\
1.5, 0.5 & 69.8 & 50.6 & 71.6 & 52.9 & 72.2 & 52.8 \\ \hline
**ours (0.3, 1.7)** & **71.2** & **51.3** & **73.2** & **53.2** & 72.5 & 52.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Influence of different parameters \(\lambda_{1}\), \(\lambda_{2}\) on the DukeMTMC-reID dataset. mAP, mean average precision.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\omega\) & **Rank-1 (EU)** & **Rank-1 (XQ)** & **Rank-1 (KI)** \\ \hline
0.02 & 70.6 & 71.8 & **72.6** \\
0.1 & 70.5 & 72.6 & 72.5 \\
0.2 & 70.4 & **73.2** & 72.5 \\
0.3 & 69.8 & 71.8 & 71.6 \\ \hline
**ours (0.04)** & **71.2** & **73.2** & 72.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The influence of \(\omega\) on the DukeMTMC-reID dataset with the BL + Euclidean, BL + XQDA, and BL + KISSME.  
the attention map in the attention-aware adversarial network inherited the ability to highlight nearly all attentive regions of the target person.  
In this section, we experimentally analyze if the attention map trained in the attention-aware adversarial network was able to capture as many important regions as possible. Namely, we did not fine-tune the attention-aware adversarial network with the parameters of the attention assignment network; instead, we simply occluded one-third of the feature map regions with the highest response. As shown in Table 4, fine-tuning the attention-aware adversarial network with the pre-trained network was able to achieve more favorable performance. The visual occlusion results of the attention map generated in the attention-aware adversarial network are illustrated in Figure 4. According to the results, the occlusion samples presented diversity. Multiple parts of important regions were occluded for the adversarial process. Therefore, the attention-aware adversarial network with fine-tuning operation outperformed the one without fine-tuning and improved the performance of the baseline network by a large margin. As shown in Table 5, the attention-aware adversarial network also outperformed the network with random erasing.