## 3 Experiments
### CUHK03 Experiments  
Table 5 shows the trends that similar to Table 4. However, the rank-1 accuracy achieved on CUHK03 is lower than that of current cutting-edge models by a bigger margin when compared to Market1501 experiments. This can be explained by poor lighting, thus resulting in color becoming a less reliable criterion. The effects shown in Figure 5 are also common for the CUHK03 dataset. However, we still outperform several older DL models. It should be stressed that the results in Table 5 are obtained by using exactly the same model as the results in Table 4. In regard to this, the importance of this fact is discussed further in Section 4.4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Model** & **Backbone** & **Human Parsing** & **Learning Type** & **Rank-1** & **Rank-10** & **mAP** \\ \hline SMI [52] \({}^{\dagger}\)19 & ResNet-50 & No & US \({}^{\star}\) & 67.7 & - & 40 \\ SIV [53] \({}^{\dagger}\)17 & ResNet-50 & No & S & 79.51 & - & 59.87 \\ MSCAN [54] \({}^{\dagger}\)17 & Custom & No & S & 80.31 & - & 57.53 \\ CAP [55] \({}^{\dagger}\)21 & ResNet-50 & No & US & 91.4 & 97.7 & 79.2 \\ SSP [34] \({}^{\dagger}\)18 & ResNet-50 & Yes & S & 92.5 & - & 80 \\ SPReID [25] \({}^{\dagger}\)18 & Inception & Yes & S & 94.63 & 98.4 & 90.96 \\ Pyramid [56] \({}^{\dagger}\)19 & ResNet & No & S & 95.7 & 99 & 88.2 \\ APNet-C [19] \({}^{\dagger}\)21 & ResNet-50 & No & S & 96.2 & - & 90.5 \\ CTL-S [57] \({}^{\dagger}\)21 & ResNet-50 & No & S & 98 & 99.5 & 98.3 \\ Ours & ResNet-101 (parser) & Yes & A & 91 & 96 & 25.2 \\ Ours (cq) & ResNet-101 (parser) & Yes & A & 93.5 & 98.0 & 25.3 \\ \hline \end{tabular} \({}^{\star}\)S–supervised, US—unsupervised, and A—analytical, no learning.  
\end{table}
Table 4: Performance comparison on the Market1501 dataset.  
Figure 5: Similarly dressed people (**right**), in a test set of the Market1501 dataset, that have similar scores for a query (**left**), thereby resulting in a low mAP.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Model** & **Backbone** & **Human Parsing** & **Learning Type** & **Rank-1 (L)** & **mAP (L)** & **Rank-10 (D)** & **mAP (D)** \\ \hline HA-CNN [58] \({}^{\dagger}\)18 & Inception & No & WS \({}^{\star}\) & 44.4 & 41 & 41.7 & 38.6 \\ DaRe [59] \({}^{\dagger}\)18 & DenseNet-201 & No & S & 56.4 & 52.2 & 54.3 & 50.1 \\ DaRe [59] \({}^{\dagger}\)18 & DenseNet-201 & No & S + RR & 73.8 & 74.7 & 70.6 & 71.6 \\ SSP [34] \({}^{\dagger}\)18 & ResNet-50 & Yes & S & 65.6 & 63.1 & 66.8 & 60.5 \\ OSNet [60] \({}^{\dagger}\)19 & OSNet & No & S & - & 72.3 & 67.8 \\ Top-DB-Net [35] \({}^{\dagger}\)20 & ResNet-50 & Yes & S & 79.4 & 75.4 & 77.3 & 73.2 \\ Top-DB-Net [35] \({}^{\dagger}\)20 & ResNet-50 & Yes & S + RR & 88.5 & 86.7 & 86.9 & 85.7 \\ MPN [61] \({}^{\dagger}\)21 & ResNet-50 & No & S & 85 & 81.1 & 83.4 & 79.1 \\ Deep Miner [62] \({}^{\dagger}\)21 & RedNet-50 & No & S & 86.6 & 84.7 & 83.5 & 81.4 \\ LightMBN [63] \({}^{\dagger}\)21 & OSNet & No & S & 87.2 & 85.1 & 84.9 & 82.4 \\ Ours & Resnet-101 (parser) & Yes & A & 61.1 & 20.9 & 59.7 & 20.2 \\ Ours (cq) & Resnet-101 (parser) & Yes & A & 63.9 & 22.1 & 62.2 & 21.4 \\ \hline \end{tabular}
\end{table}
Table 5: Performance comparison on CUHK03 dataset.