# RANDnet: Vehicle Re-Identification with Relation Attention and Nuance-Disparity Masks
## 4 Experiments
### Ablation Study  
In this section, we present a series of ablation studies designed to assess the performance enhancements brought forth by our method. Utilizing the VeRi-776 dataset, the results of these studies are documented in Table 2.  
(1) Effectiveness of the N&D masks. The results showcased in Table 2 reveal that our N&D masks register an mAP of 83.6%, thereby achieving a notable improvement of 1.7% over the established baseline. Unlike the baseline model, which tends to be misled by irrelevant background objects such as guardrails and green belts, the model incorporating the N mask directs its focus more accurately onto the vehicle body, effectively ignoring these distractions. Furthermore, the N mask fosters a more refined attention distribution, whereas the D mask expands the focus areas to prevent overly concentrated responses, demonstrating the different benefits of each mask. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(\beta\) & **mAP** & **CMC@1** & **CMC@5** \\ \hline
0 & 0.816 & 0.957 & 0.983 \\
0.01 & 0.817 & 0.954 & 0.985 \\
0.05 & 0.830 & 0.964 & 0.985 \\
0.10 & **0.842** & 0.981 & **0.995** \\
0.20 & 0.824 & **0.982** & 0.986 \\
1.00 & 0.818 & 0.973 & 0.985 \\ \hline \hline \end{tabular} Bold numbers indicate the best results.  
\end{table} 
Table 1: Parameter analysis of \(\beta\). Figure 10: Experimental results of parameter analysis on \(L\) and \(d_{LA}\). We conducted a visual analysis of the sorting results to demonstrate the effectiveness of our method. In Figure 11, we present several examples where the re-identification results were poor in the baseline. Compared to the baseline, the use of the N&D mask allows the model to suppress interference from background and foreground noise, as illustrated in the comparison of the results in the first two rows of the figure. Additionally, by enhancing focus on less discriminative regions, the model can adaptively mine key components related to the ID, such as the sticker on the windshield of the minivan and the roof of the cargo truck, as shown in the third and fourth rows. We further conducted a visual analysis of the feature maps, as shown in Figure 12. Through the implementation of the D mask, the network's focus is centralized on regions with the highest ID discriminability, rather than on the background. However, this approach results in an excessively small area of attention. By applying the N mask, the network expands its focus to include sub-discriminative regions, thereby enhancing the overall effectiveness of the identification process. Figure 11: Visualization of ranking list on vehicle ReID task. The image on the left is the query image and the red/blue boxes indicate right/wrong results. Figure 12: The visualization of the class activation areas learned by the network is displayed, where the second column shows the regions learned by the baseline, the third column presents those learned after applying the D Mask, and the fourth column illustrates the regions discerned post-application of the N&D Mask. This representation provides a comparative insight into the model’s learning progression and the effectiveness of the employed masking techniques. (2) Effectiveness of the Relation Attention Module. Table 2 indicates that incorporating the relation attention module elevates the model's mAP to 81.6% and CMC@1 to 96.3%, outstripping the baseline by margins of 2.3% and 0.6%, respectively. These enhancements underscore the module's capacity to facilitate the model's understanding of the interconnections between different vehicle components, thereby significantly boosting the re-identification precision. Additionally, to gauge the model's resilience to variations in vehicle pose, we curated a validation dataset comprising 517 manually selected vehicle image pairs from VeRi-776, each pair showcasing different angles of the same vehicle. By calculating the cosine distance between the embeddings of each image pair, we were able to evaluate the model's pose adaptability.