# A Novel Dual Mixing Attention Network for UAV-Based Vehicle Re-Identification
## 4 Analysis and Experiments
### Ablation Experiment and Analysis
#### 4.4.3 The Effect of Normalized Strategy in Dual Mixing Attention Module  
To study the effect of the normalized strategy in our proposed DMAM, we replace the adaptive average pool (AAP) in DMAM with adaptive max pooling (AMP) or group norm (Gn) with instances norm (IN). Table 5 shows the ablation experiment performed on the VeRi-UAV dataset and draws three conclusions.  
(1) First, the AMP normalization strategy is 4.84% higher than the baseline on the mAP. Secondly, AMP can enhance the representation ability of input features by selecting the most significant features for pooling. By highlighting the most discriminative and essential features, AMP helps extract critical information from vehicle images and strengthens the discrimination of vehicle appearance. Third, using AAP in DMAM improves mAP by 1.34% compared to AMP. Finally, AMP selects the most salient features in the input feature map for pooling, which means that other minor but still significant features are ignored. It may result in some key details and distinct loss features, reducing the vehicle image discriminant ability model.  
(2) First, the IN normalization strategy is 5.58% higher than the baseline on the mAP. Secondly, IN is mainly used to normalize a single sample. The features of each sample distribution are more stable. In vehicle ReID, IN can be used to normalize the feature representation of each vehicle image to improve the network's performance. Third, using Gn in DMAM improves mAP by 0.6% compared to IN. Finally, the Gn characteristics of each group in normalization reduce the mutual influence between the characteristics of the channel. It makes the group norm changes for the batch size more robust in training and testing the model to maintain a stable performance. However, the batch size of IN is small, which may lead to a larger variance, introducing some instability.  
(3) First, the DMANet is 6.18% higher than the mAP baseline. Secondly, the normalization strategy used in DMAM is shown in Figure 3. Gn is used to extract the spatial  
Figure 4: **Ablation experiments with different backbone networks.** Adding dual mixing attention module (DMAM) at different residual blocks of the backbone network on VeRi-UAV (%).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Rank-1** & **Rank-5** & **Rank-10** & **mAP** \\ \hline Baseline & 70.94 & 84.56 & 88.22 & 60.04 \\ AAP \(\rightarrow\) AMP & 74.88 & 87.21 & 90.49 & 64.88 \\ GN \(\rightarrow\) IN & 75.98 & 88.03 & 91.25 & 65.62 \\
**Ours** & **76.63** & **88.54** & **91.75** & **66.22** \\ \hline \hline \end{tabular}
\end{table}
Table 5: The effect of normalized strategy in dual mixing attention module (DMAM).  
features of the feature map, and AAP is used to extract the channel features of the feature map. In the image of a vehicle, regions at different locations have different textures, shapes, or detailed information, and Gn makes the network more robust in responding to different spatial locations. AAP can preserve key vehicle features and ignore unimportant details.