# A Multi-Scale Graph Attention-Based Transformer for Occluded Person Re-Identification
## 4 Experiments and Results
### Results of Dataset Verification  
We compared our approach with various state-of-the-art methods on the occluded person re-identification benchmark, as listed in Table 2. The comparison encompassed three  
Figure 3: Samples of images acquired using the data augmentation techniques employed in our approach: random cropping and random erasing.  
categories of methods: multi-scale convolutional approaches, methods utilizing spatial key point information, and those based on the transformer architecture. The results of our experiments demonstrate that our method outperforms other methods in occluded scenarios, excelling in extracting more effective features compared to its counterparts. Figure 4 shows the mAP improvement curve of our proposed method and the decrease in loss during the training process. Figure 5 shows the ROC curves of our model on different datasets. 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.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & \multicolumn{2}{c}{**Occ\_Duke**} & \multicolumn{2}{c}{**Occ\_ReID**} & \multicolumn{2}{c}{**Partial\_ReID**} \\ \hline  & **Method** & **mAP** & **Rank-1** & **mAP** & **Rank-1** & **mAP** & **Rank-1** \\ \hline \multirow{3}{*}{Multi-scale CNN} & DSR [30] & 30.4 & 40.8 & 62.8 & 72.8 & - & 50.7 \\ \cline{2-8}  & FPR [31] & - & - & 68.0 & 78.3 & - & 68.1 \\ \hline \multirow{3}{*}{Transformer} & TransReID [6] & 55.7 & 64.2 & 67.3 & 81.6 & - & 68.6 \\ \cline{2-8}  & DC-former [5] & 56.6 & 63.3 & 45.7 & 49.0 & - & 73.0 \\ \cline{2-8}  & PAT [9] & 53.6 & 64.5 & 72.1 & 70.2 & - & - \\ \cline{2-8}  & PVT [10] & **57.6** & 65.5 & 74.0 & 79.1 & - & 81.0 \\ \hline \multirow{3}{*}{Spatial key point graph} & PVPM [13] & - & - & 61.2 & 70.4 & 71.4 & 78.3 \\ \cline{2-8}  & OAMN [14] & 46.1 & 62.6 & - & - & - & **86.0** \\ \cline{2-8}  & PAFM [15] & 42.3 & 55.1 & 68.0 & 76.4 & - & 82.5 \\ \cline{2-8}  & HOReID [11] & 43.8 & 55.1 & 70.2 & 80.3 & - & 85.3 \\ \cline{2-8}  & RFCNet [16] & 54.5 & 63.9 & - & - & - & - \\ \cline{2-8}  & EcReID [12] & 52.7 & 64.8 & 75.1 & **84.5** & - & 81.0 \\ \hline \multirow{3}{*}{Others} & QPM [32] & 49.7 & 64.4 & - & - & - & 81.7 \\ \cline{2-8}  & MoS [33] & 55.1 & 66.6 & - & - & - & - \\ \cline{2-8}  & BMM [34] & 55.6 & 63.4 & - & - & - & 73.7 \\ \hline \multirow{3}{*}{Others} & ours* & 56.9 & 65.5 & **79.3** & 83.0 & **81.5** & 84.9 \\ \cline{2-8}  & ours & 57.1 & **66.8** & 77.2 & 81.3 & 80.8 & 85.3 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison with state-of-the-art methods on occluded ReID datasets. The label ours* denotes configurations where the number of multi-head attention heads is set to four, while that for ours was set to eight (the best results are shown in bold, and the second-best result is underlined).  
Figure 4: mAP curve and loss curve during training on different datasets. From left to right are the situations on the occ_duke, occ_reid, and partial_reid datasets. The first row of three images labeled as’ (**a**) display changes in mAP metrics on the test set. The second row of three images labeled as’ (**b**) display changes in loss on the train set.