# RANDnet: Vehicle Re-Identification with Relation Attention and Nuance-Disparity Masks
## 4 Experiments
### Comparison with State-of-the-Art  
We compare our method with recent state-of-the-art methods, including RAM [20], VAMI [16], AAVER [46], PRN citepart-regularized, SAVER [25], PGAN [47], PVEN [22], and FDA-Net [48].  
Experiments are conducted on three large-scale public datasets. On VeRi-776 [43], mAP, CMC@1, and CMC@5 are adopted to evaluate the performance quantitatively, and on VehicleID [44], we apply CMC@1 and CMC@5. Table 3 presents the comparison on VeRi-776 [43] and VehicleID [44].  
_Results on VeRi-776._ Table 3 presents the comparison on VeRi-776 [43]. Our approach, RANDnet, achieves a mean Average Precision (mAP) of 83.5% and a Cumulative Matching Characteristic at Rank 1 (CMC@1) of 97.7%. These results demonstrate that our method  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**VeRi-776**} & \multicolumn{6}{c}{**VehicleID**} \\ \cline{2-10}  & **mAP** & **CMC@1** & **CMC@5** & **CMC@1** & **CMC@5** & **CMC@1** & **CMC@5** & **CMC@1** & **CMC@5** \\ \hline VAMI [16] & 0.501 & 0.770 & 0.908 & 0.631 & 0.833 & 0.529 & 0.751 & 0.473 & 0.703 \\ AAVER [46] & 0.612 & 0.890 & 0.947 & 0.747 & 0.938 & 0.686 & 0.900 & 0.635 & 0.856 \\ EALN [49] & 0.574 & 0.844 & 0.941 & 0.751 & 0.881 & 0.718 & 0.839 & 0.693 & 0.814 \\ RAM [20] & 0.615 & 0.886 & 0.940 & 0.752 & 0.915 & 0.723 & 0.870 & 0.677 & 0.845 \\ PRN [19] & 0.743 & 0.943 & 0.987 & 0.784 & 0.923 & 0.750 & 0.883 & 0.742 & 0.864 \\ SAVER [25] & 0.796 & 0.964 & 0.986 & 0.799 & 0.952 & 0.776 & 0.911 & 0.753 & 0.883 \\ PGAN [47] & 0.793 & 0.965 & 0.983 & - & - & - & - & 0.778 & 0.921 \\ PVEN [22] & 0.795 & 0.956 & 0.984 & 0.847 & 0.970 & 0.806 & 0.945 & 0.778 & 0.920 \\ TransReID [50] & 0.823 & 0.971 & - & 0.852 & 0.976 & - & - & - & - \\ DFNet [12] & 0.810 & 0.971 & **0.990** & 0.848 & 0.962 & 0.806 & 0.941 & 0.791 & 0.929 \\ SGFD [51] & 0.810 & 0.967 & 0.986 & 0.868 & 0.974 & 0.835 & 0.956 & 0.808 & 0.937 \\ VAL [52] & 0.814 & 0.967 & 0.987 & 0.857 & 0.970 & 0.812 & 0.950 & 0.782 & 0.930 \\ PAMNet [53] & 0.816 & 0.965 & 0.986 & 0.853 & 0.973 & 0.805 & 0.945 & 0.776 & 0.922 \\ Fast [54] & 0.819 & 0.970 & **0.990** & 0.866 & 0.979 & 0.829 & 0.960 & 0.806 & 0.939 \\ HRCN [55] & 0.831 & 0.973 & 0.989 & 0.882 & 0.984 & 0.814 & **0.966** & 0.802 & 0.944 \\ VNet [8] & 0.834 & 0.968 & - & 0.836 & 0.969 & 0.813 & 0.936 & 0.795 & 0.920 \\ \hline Baseline(ibn-50) & 0.819 & 0.970 & 0.989 & 0.866 & 0.979 & 0.829 & 0.960 & 0.806 & 0.939 \\ Ours & **0.835** & **0.977** & **0.990** & **0.885** & **0.987** & **0.833** & 0.965 & **0.810** & **0.945** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results comparison on VeRi-776 and VehicleID.  
significantly surpasses other state-of-the-art techniques by a considerable margin. Notably, there is a substantial improvement in the mAP, whereas the gains in CMC@1 and CMC@5 are relatively modest. This indicates a strong overall performance, particularly in accurately matching the top-ranked vehicle identities. We attribute this phenomenon to the fact that incorporating additional secondary discriminative features assists the network in elevating the accuracy of re-identifying targets belonging to similar vehicle models. However, the impact on differentiation between two categories that are extremely similar is relatively limited.  
_Results on VehicleID_. The evaluation results are shown in Table 3. Our method achieves 88.5%, 83.3%, and 81.0% in CMC@1 on three test sets. Meanwhile, RANDnet achieves 98.7%, 96.5%, and 94.5% in CMC@5. It is clear that our method outperforms other recent works in almost all metrics. Our method demonstrates a substantial improvement in the Rank 1 metric on this dataset. We attribute this enhancement to the fact that the VehicleID dataset comprises only two directional viewpoints--frontal and rear views. This circumstance allows our relation attention (RA) module to effectively mitigate feature distance disparities between different viewpoints.