# A Person Re-Identification Method Based on Multi-Branch Feature Fusion
## 4 Experiments
### Ablation Studies  
Effects of different branches in the model. We conduct experiments on the effects of each branch of the model on the MSMT17 dataset. As depicted in Table 3, the combination of different branches significantly improves the accuracy of recognition. In the two-branch network, the saliency guidance local branch substantially boosts model performance. Notably, the MFFNet model incorporating all the three branches achieve the highest performance. This highlights the diverse features extracted by the two local branches and validates the effectiveness of each branch. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Standard Scene** & **Occlusion Scene** & **The Scene with Illumination Difference and Scale Change** \\ \hline Input & & & \\ ResNet50 [48] & & & \\ Swin Transformer & & & \\ [37] & & & \\ MFFNet & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Heat maps of visualization results of feature extraction based on Grad-CAM. Figure 8: Visualization results of MFFNet on Market-1501. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Rank-1/\%** & **mAP/\%** \\ \hline OSNet & 78.3 & 52.8 \\ OSNet + Standard Fusion Attention Module & 78.9 & 53.6 \\ OSNet + HAM & 79.1 & 53.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Effects of different modules in the saliency guidance branch. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Rank-1/\%** & **mAP/\%** \\ \hline \hline
\begin{tabular}{c} **Multi-scale Fusion global branch** \\ **Multi-scale Fusion global branch + Hybrid Attention local branch** \\ **Multi-scale Fusion global branch + Saliency Guidance local branch** \\ **Multi-scale Fusion global branch + Hybrid Attention local branch + Saliency Guidance local branch** \\ **Multi-scale Fusion global branch + Hybrid Attention local branch + Saliency Guidance local branch** \\ **80.2** & 56.6 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Effect of different modules on the global branch. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Rank-1/\%** & **mAP/\%** \\ \hline
\begin{tabular}{c} **Multi-scale Fusion global branch** \\ **Multi-scale Fusion global branch + Hybrid Attention local branch** \\ **Multi-scale Fusion global branch + Saliency Guidance local branch** \\ **Multi-scale Fusion global branch + Hybrid Attention local branch + Saliency Guidance local branch** \\ **80.2** & 56.6 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effects of different branches in the MFFNet model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Rank-1/\%** & **mAP/\%** \\ \hline
\begin{tabular}{c} **Soft** \\[PERSON] et al. [7] utilizes saliency model-based multi-scale feature fusion to extract significant body regions from images to mitigate interference from complex backgrounds in person re-ID tasks. [PERSON] et al. [25] presented a Feature Erasing and Diffusion Network capable of addressing both non-target pedestrian occlusions and non-target pedestrians. [PERSON] et al. [34] combined the complementary advantages of CNN and Transformer, using feature enhancement and pose-guided modules to tackle challenges posed by poses and occlusions in person Re-ID. Compared to these methods, our approach takes into account various complex factors that influence a person re-ID. In terms of the experimental results, on DukeMTMC-reID, MFFNet outperformed recent methods in both Rank-1 and mAP. On Market1501, Rank-1 of MFFNet is 0.5% lower than [25] and 1.2% lower than [34]. Comparisons of specific experimental results are shown in Figures 9 and 10. This indicates that MFFNet demonstrates superior recognition accuracy in typical scenarios, accurately matching the correct identities of pedestrians. However, in simple scenarios, it falls slightly behind [25; 34] in the overall evaluation of all query results.