# Stripe-Assisted Global Transformer and Spatial-Temporal Enhancement for Vehicle Re-Identification
## 4 Experiment
### Ablation Study and Analysis
#### 4.4.3 Visualization Analysis  
In addition to the ablation study of our proposed method, we visualized the results of vehicle re-identification and Grad-CAM of attention maps to further analyze its effectiveness. Figure 3 presents the top-10 retrieval results of three query images from VeRi-776, where green and red borders denote correct and incorrect retrieval results, respectively. It is clear that both Baseline and SaGT achieved promising performance in scenarios with slight viewpoint variations (e.g., Figure 3b,c). When vehicle images shared similar appearances, SaGT, capturing crucial local details for accurate identification, yielded a larger number of correct retrieval results than Baseline (e.g., Figure 3b). Nevertheless, in cases involving occlusions and extremely similar appearances (e.g., Figure 3a,c), both Baseline and SaGT, relying solely on visual features, generated great error matches. The introduction of spatial-temporal cues in SaGT-ST significantly improved performance for these challenging scenarios.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{3}{*}{**Method**} & \multicolumn{2}{c}{**VeRi-776**} & \multicolumn{6}{c}{**VehicleID**} \\ \cline{3-8}  & & \multicolumn{3}{c}{**Small**} & \multicolumn{2}{c}{**Medium**} & \multicolumn{2}{c}{**Large**} \\  & **mAP** & **Rank-1** & **mAP** & **Rank-1** & **mAP** & **Rank-1** & **mAP** & **Rank-1** \\ \hline Baseline & 78.38 & 95.71 & 85.06 & 77.38 & 81.63 & 74.13 & 78.04 & 69.75 \\ SaGT-SFM & 80.30 & 96.96 & 89.66 & 84.00 & 86.01 & 80.09 & 83.84 & 77.67 \\ SaGT & **80.67** & **96.96** & **91.36** & **86.33** & **87.30** & **81.44** & **84.38** & **78.13** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The ablation study (%) of SFM and SaGL on VeRi-776 and VehicleID. The best performance is marked in bold.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Feature**} & **mAP \(\uparrow\)** & **Parameter \(\downarrow\)** & **Time \(\downarrow\)** & **Storage \(\downarrow\)** \\  & & **(\%)** & **(M)** & **(ms/Image)** & **(KB/Image)** \\ \hline \multirow{3}{*}{SaGT-SFM} & GF & 80.30 & 85.6 & 8.13 & 6 \\  & LF & 80.33 & 85.7 & 13.45 & 48 \\  & GF and LF & 80.34 & 92.7 & 15.17 & 54 \\ \hline SaGT & GF & 80.67 & 85.6 & 8.13 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The performance of different features for inference on VeRi-776.  
Figure 4 shows the Grad-CAM [42] visualization of attention maps. Compared to the Baseline, SaGT paid more attention to local regions with key details, which can provide more discriminative information for the global representation of vehicles. However, both baseline and SaGT were interfered with by irrelevant information, especially the road sign, in the background.  
Figure 4: **The Grad-CAM visualization of attention maps.** The first, second, and third rows show the original image, Baseline, and SaGT, respectively.  
Figure 3: **Re-identification visualization examples on VeRi-776.** For each query, the three rows from top to bottom show the top-10 retrieval results produced by Baseline, SaGT, and SaGT-ST, respectively.