# Multi-Level Fusion Model for Person Re-Identification by Attribute Awareness
## 4 Experiment
### Comparison with the State-of-the-Art  
We compare our method with methods published in recent years. Tables 1 and 2 compare the Rank1, Rank5, Rank10, and mAP evaluation metrics of the Market-1501 and DukeMTMC-reID datasets, respectively, to those of these other methods, where \"-\" means there is no record. For the Market-1501 dataset, the methods compared are GAN-based, part-based, and combined. From Table 1, we find that our proposed method achieved the best results on Rank1, Rank5, Rank10, and mAP when compared with current state-of-the-art methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Rank1** & **Rank5** & **Rank10** & **mAP** \\ \hline \hline \multicolumn{1}{c}{BoW + kissme [28]} & 25.13 & - & - & 12.17 \\ LOMO + XQDA [56] & 30.75 & - & - & 17.04 \\ \multicolumn{1}{c}{AttrCombine [34]} & 53.87 & - & - & 33.35 \\ GAN [30] & 67.68 & - & - & 47.13 \\ SVDNet [39] & 76.7 & - & - & 56.8 \\ APR [6] & 73.92 & - & - & 55.56 \\ PSE [48] & 79.8 & 89.7 & 92.2 & 62 \\ DistributionNet [49] & 74.73 & 85.05 & 88.82 & 55.98 \\ AttKGCN [51] & 87.8 & 94.4 & 95.7 & 77.4 \\ Yin [7] & 82.7 & 91 & 93.5 & 66.4 \\ SCSN (4 stages) [52] & 91.0 & - & - & 79.0 \\ SLAMH [53] & 90.1 & - & - & 79.4 \\ Jin [24] & 88.6 & - & - & 78.4 \\ Zhou [54] & 88.7 & - & - & 76.6 \\ Li [55] & 90.2 & - & - & 79.7 \\ MLAReID & 91.4 & 95.5 & 96.7 & 81.4 \\ MLAReID + Rerankingg & 92.7 & 96.1 & 97.2 & 90.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with the state-of-the-art algorithms on DukeMTMC-reID. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Rank1** & **Rank5** & **Rank10** & **mAP** \\ \hline \hline \multicolumn{1}{c}{BoW + kissme [28]} & 25.13 & - & - & 12.17 \\ LOMO + XQDA [56] & 30.75 & - & - & 17.04 \\ AttrCombine [34] & 53.87 & - & - & 33.35 \\ GAN [30] & 67.68 & - & - & 47.13 \\ SVDNet [39] & 76.7 & - & - & 56.8 \\ APR [6] & 73.92 & - & - & 55.56 \\ PSE [48] & 79.8 & 89.7 & 92.2 & 62 \\ DistributionNet [49] & 74.73 & 85.05 & 88.82 & 55.98 \\ AttKGCN [51] & 87.8 & 94.4 & 95.7 & 77.4 \\ Yin [7] & 82.7 & 91 & 93.5 & 66.4 \\ SCSN (4 stages) [52] & 91.0 & - & - & 79.0 \\ SLAMH [53] & 90.1 & - & - & 79.4 \\ Jin [24] & 88.6 & - & - & 78.4 \\ Zhou [54] & 88.7 & - & - & 76.6 \\ Li [55] & 90.2 & - & - & 79.7 \\ MLAReID & 91.4 & 95.5 & 96.7 & 81.4 \\ MLAReID + Rerankingg & 92.7 & 96.1 & 97.2 & 90.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison with the state-of-the-art algorithms on Market-1501. For the DukeMTMC-reID dataset, compared with other methods on the evaluation metrics Rank1, Rank5, Rank10, and mAP, the method proposed in this paper achieves the best performance. For the Market-1501 dataset, this paper lists the accuracy of 12 attributes for comparison. \"age\" has four attributes: young, teenager, adult, and old. \"upcolor\" has eight attributes: upblack, upwhite, up-red, uppurple, upyellow, upgray, upblue, and upgreen. \"downcolor\" has nine attributes: downblack, downwhite, downpink, downpurple, downgray, downblue, downgreen, and downbrown. \"Upcolor\" and \"Downcolor\" in Figure 4(b) represent the average accuracy of these two sets of related attributes. It can be found from Figure 5 that recognition rate of \"Backpack\", \"Bag\", \"Hat\", and \"Up\" are not as good as the method proposed by [PERSON] [7], but the recognition rates of the other attributes are significantly improved. For the DukeMTMC-reID dataset, we list the accuracy of 10 attributes for comparison. \"upcolor\" has eight attributes: upblack, upwhite, uppurple, upgray, upblue, upgreen, and upbrown. \"downcolor\" has seven attributes: downblack, downwhite, downred, downgray, downblue, downgreen, and downbrown. \"Upcolor\" and \"Downcolor\" in Figure 5(b) represent the average accuracy of these related attributes. It can be found from Figure 6 that, except for the attributes of \"Backpack\", \"Bag\", and \"Top\", the recognition rates of the attributes are greatly improved compared to the method proposed by [PERSON] [7].