# Feature-Level Camera Style Transfer for Person Re-Identification
## 5 Experiments
### Comparison with Baselines and State-of-the-Arts  
Experimental results are shown in Table 1. Despite the high baseline adopted in this experiment, the proposed methods still achieve remarkable improvement over the previous works. Compared to the IDE baseline with Cross Entropy (CE) loss (IDC-CE), by introducing CST, the mAP and Rank-1 improve by \(+3.9\%\) and \(+2.2\%\). When compared to IDE with triplet loss (IDE-Trip) and IDE with both losses (IDE-CE-Trip), a similar increase can be observed. The authors further illustrate the performance of the proposed method adapted to the PCB baseline; the performance of the proposed CST surpasses the baseline by \(+1.6\%\) in mAP and \(+0.9\%\) in Rank-1. For the MGN baseline, the CST approach also outperforms the baselines in both mAP and Rank-1. Some state-of-the-art methods are also listed in Table 2 for comparison on the Market-1501 dataset. The first group includes methods that did not use generative models as data augmentation, and the second group includes GAN-based methods that use generated or style transferred images. As we can see, the proposed CST applied to any baseline method outperforms most of the other SOTA methods. These quantitative results show that the proposed CST approach is suitable for both global and partial features; thus, it can be applied in multiple person re-ID baselines, and it outperforms state-of-the-art methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Market-1501** & **mAP** & **Rank-1** & **Rank-5** & **Rank-10** \\ \hline IDE-CE [1] & 75.0 & 89.1 & 96.4 & 97.9 \\
**IDE-CE + CST** & **78.9** & **91.3** & **97.4** & **98.5** \\ IDE-Trip & 74.9 & 88.4 & 95.4 & 96.8 \\
**IDE-Trip + CST** & **76.7** & **89.0** & **95.6** & **97.0** \\ IDE-CE-Trip & 79.8 & 91.6 & 97.0 & 98.1 \\
**IDE-CE-Trip+ CST** & **82.3** & **92.8** & **97.6** & **98.7** \\ PCB [7] & 77.4 & 92.3 & 97.2 & 98.2 \\
**PCB + CST** & **79.0** & **93.2** & **97.8** & **98.8** \\ MGN * [31] & 86.3 & 93.9 & 97.3 & 98.5 \\
**MGN + CST** & **87.9** & **95.0** & **98.1** & **99.0** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The performance comparison of baseline methods and the proposed proposed CST on the Market1501 dataset. Results of the proposed methods are marked as +CST, and better results are marked in bold. * The results of MGN are reported by our reproducing. Figure 7: Hyper-parameter analysis of the weight of generated feature loss \(\lambda_{gen}\) on Market-1501. Best performance achieved when \(\lambda_{gen}=0.5\). Furthermore, some qualitative comparisons are presented to vividly show the improvement brought by the proposed CST approach. Figure 8 shows the retrieved results of two example query images in the Market-1501 dataset. As we can see in the figure, the baseline model tends to be affected by camera style-related factors, e.g., posture, background, illumination, etc. The proposed CST can generate a large amount of camera style transferred features to alleviate this problem so that the model can overcome the effect of camera style variance and perform more accurate matching. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Market-1501** & **mAP** & **Rank-1** & **Rank-5** & **Rank-10** \\ \hline LGMANet [33] & 82.7 & 94.0 & - & - \\ MTNet [34] & 81.5 & 93.9 & - & - \\ RIN [35] & 67.6 & 86.1 & - & - \\ RNLSTM [36] & 76.9 & 90.6 & - & - \\ Gconv [37] & 72.3 & 88.1 & 95.1 & 96.8 \\ Top-DB-Net [38] & 85.8 & 94.9 & - & - \\ DTr [39] & 76.7 & 91.2 & - & - \\ MSFANet [40] & 82.3 & 92.9 & - & - \\ \hline DeformGAN [41] & 61.3 & 80.6 & - & - \\ LRSO [42] & 66.1 & 84.0 & - & - \\ CamStyle [14] & 71.6 & 89.5 & - & - \\ FD-GAN [43] & 77.7 & 90.5 & - & - \\ DRANet [44] & 79.7 & 92.9 & 97.2 & 98.3 \\ MANet [45] & 78.8 & 93.8 & - & - \\ \hline IDE-CE-Trip + CST & 82.3 & 92.8 & 97.6 & 98.7 \\ PCB + CST & 79.0 & 93.2 & 97.8 & 98.8 \\
**MGN + CST** & **87.9** & **95.0** & **98.1** & **99.0** \\ \hline \hline \end{tabular}
\end{table}