# Stripe-Assisted Global Transformer and Spatial-Temporal Enhancement for Vehicle Re-Identification
## 4 Experiment
### Ablation Study and Analysis
#### 4.4.2 Effectiveness of SFM and SaGL  
Table 4 displays the results of the ablation study involving SFM and SaGL on VeRi-776 and VehicleID datasets. SaGT-SFM integrated an SFM into the Baseline, which trained global and local features using the triplet loss, respectively. Compared to SaGT-SFM, the final method SaGT was optimized by an SaGL instead of the triplet loss. As illustrated in  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Reference**} & \multicolumn{2}{c}{**Small**} & \multicolumn{2}{c}{**Medium**} & \multicolumn{2}{c}{**Large**} \\  & & & **mAP** & **Rank-1** & **mAP** & **Rank-1** & **mAP** & **Rank-1** \\ \hline SN [13] & GF & TNNLS’22 & 78.80 & 76.70 & 76.80 & 74.80 & 76.30 & 73.90 \\ VARID [12] & GF & TITS’22 & 88.50 & 85.80 & 84.70 & 81.20 & 82.40 & **79.50** \\ VAT [18] & GF & IPM’22 & 89.90 & 84.50 & 87.10 & 80.50 & **85.00** & 78.20 \\ MsKAT [32] & GF & TITS’22 & - & 86.30 & - & **81.80** & - & 79.40 \\ \hline DFR [38] & LF & PR’22 & 87.55 & 82.15 & 84.94 & 79.33 & 83.18 & 77.93 \\ HPGN [37] & LF & TITS’22 & 89.60 & 83.91 & 86.16 & 79.97 & 83.60 & 77.32 \\ PGAN [15] & LF & TITS’22 & - & - & - & - & 83.90 & 77.80 \\ LG-CoT [36] & LF & ICTAI’22 & 90.50 & 85.20 & 86.60 & 80.50 & 84.40 & 78.00 \\ DSN [39] & LF & PR’23 & 81.70 & 80.60 & 79.10 & 78.20 & 75.50 & 75.00 \\ SOFCT [22] & LF & TITS’23 & 89.80 & 84.50 & 86.40 & 80.90 & 84.30 & 78.70 \\ GiT [31] & LF & TIP’23 & 90.12 & 84.65 & 86.77 & 80.52 & 84.26 & 77.94 \\ SFMNet [40] & LF & IJCNN’23 & - & 85.10 & - & 80.50 & - & 77.60 \\ \hline Baseline & GF & Ours & 85.06 & 77.38 & 81.63 & 74.13 & 78.04 & 69.75 \\ SaGT & GF & Ours & **91.36** & **86.33** & **87.30** & 81.44 & 84.38 & 78.13 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performance (%) comparison on VehicleID. The best performance is marked in bold.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **Fusion** & **mAP** & **Rank-1** \\ \hline Baseline & - & 78.38 & 95.71 \\ Baseline-ST & Add & 82.94 (+4.56 ) & 98.27 (+2.56) \\ Baseline-ST & Multiply & 84.93 (+6.55) & 98.75 (+3.04) \\ \hline SaGT & - & 80.67 & 96.96 \\ SaGT-ST & Add & 84.61 (+3.94) & 98.33 (+1.37) \\ SaGT-ST & Multiply & 86.59 (+5.92) & 98.75 (+1.79) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The ablation study (%) of STPro on VeRi-776.  
Table 4, SaGT-SFM and SaGT significantly outperformed the Baseline method on VeRi-776 and VehicleID, indicating the substantial contribution of effective local feature learning to overall representation enhancement. Furthermore, SaGT exhibited superior performance compared to SaGT-SFM. The advantage can be credited to SaGL, which enables more focused attention on acquiring a discriminative global representation when the model simultaneously learns global and local features, compared to the original triplet loss.  
Table 5 shows the performance of different features for inference on VeRi-776. Time represents the average time required to extract features for one image, measured on a Tesla T4 GPU. Storage denotes the storage cost of image features on a 64-bit system. GF and LF refers to combining the global feature distance and the local feature distance for inference by taking their sum. From Table 5, compared to using either GF or LF for inference, fusing them only brought a slight improvement in mAP. This indicated a significant overlap of information between global and local features, namely, redundancy. Moreover, although the fusion or utilization of only LF for inference outperformed GF, their feature dimension also increased by almost eight times, which implied that applications associated with vehicle re-identification would demand eight times more storage space and more computing consumption. This consideration motivated us to choose only a global feature for inference, and SaGT, employing only a global feature, achieved performance similar to the fusion method in SaGT-SFM.