## 3 Model and Methods
### Ablation Study  
We performed ablation experiments on the CAP and MG modules in the APMG network, again using the MARS, iLIDS-VID, and PRID-2011 datasets. For the ablation experiment on the CAP module, we set up five experimental settings. In setting 1, we cancelled the two-layer sliding window structure and instead used a 16\(\times\)16 sliding window. In settings 2 and 3, we used two different sizes of two-layer sliding  
Figure 5: Activation visualisation results from three video sequences. window structures. In setting 4, we removed the correlation-adaptive partitioning of the feature embedding. In setting 5, we used all patches on the sequence corresponding to the position of the sliding window to calculate the correlation. To ensure the accuracy of the experiments, we kept the number of feature embeddings consistent across the five settings. The results are shown in Table 2.  
The re-identification accuracy results for settings 1, 2, and 3 show that our two-layer sliding window structure is effective. Under setting 2, the re-identification accuracy in iLIDS-VID was improved by 0.4% because the first layer adopted a 2 \(\times\) 2 sliding window, which made the generated patches more detailed. However, the 8 \(\times\) 8 sliding window greatly increased the number of network parameters needed for partitioning the feature embedding. Considering the overall performance and the number of network parameters in the three datasets, the scheme in setting 2 was discarded. Under setting 4, the re-identification accuracy decreased by as much as 4.5% in iLIDS-VID, which demonstrates the benefits of our correlation-adaptive partitioning of feature embeddings. Under setting 5, the re-identification accuracy was slightly improved for the MARS and PRID-2011 datasets because taking all patches into account when calculating the correlation increases the detail of the reference used for defining feature embeddings. However, this increased the number of network parameters and thus increased the computation effort, which introduced a large amount of information redundancy into the time series and thus decreased the robustness of the network. In iLIDS-VID, which is characterised by more complex background information, the re-identification accuracy decreased by 1.7%. In terms of the overall performance of the network on the three datasets and the number of network parameters, our CAP module was the best performer. We randomly selected two images in the MARS dataset for the visualisation of activations, as shown in Figure 6. The figure shows that the CAP module effectively partitioned the feature embeddings and improved the network's extraction of the distinguishable feature information. For the ablation experiment on the MG module, we set up four groups of experiments with varying partition granularities in window size. Setting 1 used a single size 16 \(\times\) 16 window (global-granularity token). Setting 2 was setting 1 plus two size 8 \(\times\) 16 windows (medium-grained tokens). Setting 3 was setting 2 plus four size 4 \(\times\) 16 windows (fine-grained tokens) and eight size 2 \(\times\) 16 windows. Setting 4 lacked the weighted sum \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Settings**} & \multirow{2}{*}{**Parameter Quantity**} & **MARS** & **iLIDS-VID** & **PRID-2011** \\ \cline{3-5}  & & **mAP** & **Rank-1** & **Rank-1** \\ \hline
4 \(\times\) 4 and 4 \(\times\) 4 CAP & 126 M & 88.4 & 93.6 & 97.1 \\
1. 16 \(\times\) 16 & 125 M & 84.1 & 90.7 & 95.4 \\
2. 2 \(\times\) 2 and 8 \(\times\) 8 & 148 M & 88.2 & 94.0 & 97.0 \\
3. 8 \(\times\) 8 and 2 \(\times\) 2 & 131 M & 86.7 & 92.4 & 96.3 \\
4. 4 \(\times\) 4 and 4 \(\times\) 2 no partition & 123 M & 86.2 & 89.1 & 93.2 \\
5. use all patches to calculate the correlation & 154 M & 88.5 & 91.9 & 97.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation experiment results for CAP module. Figure 6: Activation visualisation results. From left to right: original image, setting 4, and CAP.