# A Multi-Scale Graph Attention-Based Transformer for Occluded Person Re-Identification
## 4 Experiments and Results
### Comparison of Parameter Tuning  
In Table 3, we present the results of our evaluation of the effectiveness of data augmentation on the occluded datasets. To fully leverage the training information and mitigate the issue of data imbalance in the test set, three augmentation techniques were applied during the data pre-processing stage. The experimental results show that data augmentation significantly enhanced the performance of our model. For OCC_Duke, the augmentation strategies notably improved the method's efficacy, with an increase of 5.6% in mAP and 1.8% in Rank-1 accuracy. For OCC_ReID, the improvements in mAP and Rank-1 accuracy were even more pronounced, at 7.4% and 2.4%, respectively. Likewise, for Partial-ReID, the enhancements yielded an increase of 2.3% in Rank-1 accuracy. It is evident that the incorporation of data augmentation was efficacious across all three occlusion datasets.  
In Table 4, we present our evaluation of the impact of the number of layers in our proposed module on the results. The MSGA module was configured with one, two, or three layers, and the results of our experiments show that the best performance was attained when the number of layers was set to three. This result proves that deeper MSGA modules can learn more intricate feature representations, enabling the module to capture abstract ReID features.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Num of MSGA Layers**} & \multicolumn{2}{c}{**Occ\_Duke**} & \multicolumn{2}{c}{**Occ\_ReID**} & \multicolumn{1}{c}{**Partial\_ReID**} \\ \cline{2-6}  & **mAP** & **Rank-1** & **mAP** & **Rank-1** & **Rank-1** \\ \hline
1 & 56.1 & 66.3 & 76.6 & 80.8 & 85.1 \\ \hline
2 & 56.5 & 65.7 & 76.8 & 80.9 & 84.8 \\ \hline
3 & 57.1 & 66.8 & 77.2 & 81.3 & 85.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter tuning of MSGA layers.  
Figure 5: Our model’s ROC curves on the test sets of various datasets, from left to right, are occ_cuke, occ_deid, and part_deid, and their AUC values are 0.655, 0.758, and 0.782, respectively. ROC provides a visual representation of the trade-off between the True Positive Rate (TPR) and the False Positive Rate (FPR) at various threshold settings. TPR, also known as sensitivity or recall, measures the proportion of actual positive instances that are correctly identified by the model. FPR represents the proportion of actual negative instances that are incorrectly identified as positive. The Area Under the Curve (AUC) is a single scalar value that quantifies the overall performance of the model across all possible thresholds.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Occ\_Duke**} & \multicolumn{2}{c}{**Occ\_ReID**} & \multicolumn{1}{c}{**Partial\_ReID**} \\ \hline  & **mAP** & **Rank-1** & **mAP** & **Rank-1** & **Rank-1** \\ \hline ours (no\_aug) & 51.5 & 65.0 & 69.8 & 78.9 & 83.0 \\ \hline ours (aug) & 57.1 & 66.8 & 77.2 & 81.3 & 85.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of data augmentation.  
In Table 5, we present our analysis of the impact of the number of attention heads. We configured the number of heads to two, four, six, or eight. Moreover, we conducted experiments on occluded datasets. The use of multi-head attention enables GAT to concurrently capture diverse features from the input data. Each head is initialized with distinct weights. An increase in the number of heads allows for the learning of a broader range of distinct features; however, beyond a certain threshold, some heads may begin to learn redundant features, duplicating information already captured by others which, in turn, leads to a decline in performance.