## 5 Experiments
### Experimental Results and Analysis
#### 5.2.2 Comparison with Other Mainstream Re-ID Methods  
To validate the efficacy of our proposed method, we conducted experiments on the Market-1501, DukeMTMC-reID, and MSMT17 datasets, and compared the results with the current state-of-the-art person Re-ID methods summarized in Table 3. Among these methods, refs. [38; 39; 40; 41] are attention-based methods, wherein [38; 39] employ first-order attention to guide the network's focus onto salient features within pedestrian images, whereas [40; 41] leverage higher-order attention mechanisms to mimic and harness high-order statistical information, thereby enhancing the network's capability to distinguish similar features. Refs. [6; 24; 42; 43; 44; 45] exemplify part-based Re-ID methodologies, wherein pedestrians are segmented into distinct parts based on predefined rules, and then extract features from different segments and fuse them to enrich the feature representation. Specifically, ref. [42] is driven by pose estimation, while [6; 24; 43; 44; 45] rely on manually defined partitions. Methods [12; 46; 47; 48; 49] incorporate pedestrian masks and utilize the image foreground as input to mitigate background interference. Refs. [50; 51; 52] incorporate attribute information of pedestrians into the network, further enhancing feature representation. Refs. [53; 54; 55; 56; 57; 58; 59; 60] improve the re-identification performance through alignment, interaction, and aggregation operations on multi-dimensional features. The following is a detailed comparison of the experimental results on each dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Baseline** & **EFEM** & **IREM** & **Rank-1 (\%)** & **mAP (\%)** \\ \hline \(\surd\) & & & 92.5 & 85.1 \\ \(\surd\) & \(\surd\) & & 93.8 & 86.9 \\ \(\surd\) & & \(\surd\) & 95.1 & 88.0 \\ \(\surd\) & \(\surd\) & \(\surd\) & 96.7 & 90.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The ablation experimental results on the Market-1501 dataset. **Results on Market-1501. ** Our method achieves the best Rank-1 accuracy of 96.7% and the second-highest mAP accuracy of 90.1% on the Market-1501 dataset. Ref. [58] reports a second-best Rank-1 accuracy of 96.1%, albeit with a mAP accuracy of 88.0%, which is 1.9% lower than ours. Meanwhile, ref. [24] attains the optimal mAP accuracy of 92.1%, but its Rank-1 accuracy stands at 94.5%, trailing ours by 2.2%. **Results on DukeMTMC-reID. ** Despite the greater challenges posed by this dataset, which encompasses a broader range of viewpoint variations and illumination differences, our proposed method demonstrates strong performance as well. On the DukeMTMC-ReID dataset, our method achieves the top-ranking accuracy of 90.3% on the Rank-1 metric, marking a new state-of-the-art by outperforming the previous best method by a margin of 0.3%. In terms of mAP, our method attains 81.4% accuracy, which is 5.1% lower than the optimal performance but it still significantly outperforms the other existing methods and holds the second-best place. **Results on MSMT17. ** Our method also shows remarkable performance on the larger and more complex MSMT17 dataset, achieving 79.2% Rank-1 accuracy and 55.3% mAP accuracy. Although [59] obtained the optimal Rank-1 accuracy of 79.5%, which is 0.3% higher than ours, its mAP accuracy is 52.5%, which is 2.8% lower than our method. In addition, ref. [58] achieved the highest mAP accuracy of 55.7%, which is 0.4% higher than our method. Nevertheless, our approach is at least 2.4% higher than all other methods, ensuring the second-best position in terms of overall performance. In summary, our method achieves optimal or near-optimal results in large-scale datasets and complex environments. These experiments prove the effectiveness and versa