# Minimizing Maximum Feature Space Deviation for Visible-Infrared Person Re-Identification
## 4 Experiments and Analysis
### Role of Minimizing Feature Space Maximum Deviation Loss Function  
We adjust the \(\lambda\) in Equation (9) to evaluate the role of the MFSMD loss function. Table 3 and Figure 3 show the performance on SYSU-MM01 dataset, and Table 4 and Figure 4 show the performance on RegDB dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Visible-to-Infrared Retrieval**} & \multicolumn{2}{c}{**Infrared-to-Visible Retrieval**} & \multirow{2}{*}{**Reference**} \\ \cline{2-2} \cline{4-6}  & **Rank1 (\%)** & & & & **mAP (\%)** & \\ \hline BDTR [3] & 33.47 & 31.83 & N/A & N/A & IJCAI 2018 \\ D\({}^{2}\)RL [27] & 43.3 & 44.1 & N/A & N/A & CVPR 2019 \\ D-HTSME [43] & 50.85 & 47.00 & 50.15 & 46.16 & AAAI 2019 \\ AlignGAN [28] & 57.9 & 53.6 & 56.3 & 53.4 & ICCV 2019 \\ eBDTR [13] & 31.83 & 33.18 & N/A & N/A & TIFS 2020 \\ CE\({}^{2}\)L [4] & 47.50 & 44.21 & N/A & N/A & ICPR 2020 \\ DCD-MSR [44] & 48.43 & 48.67 & N/A & N/A & TIP 2020 \\ JSIA-ReID [23] & 48.5 & 49.3 & 48.1 & 48.9 & AAAI 2020 \\ MSPAC-MeCen [18] & 49.61 & 53.64 & N/A & N/A & ICPR 2020 \\ X modality [20] & 62.21 & 60.18 & N/A & N/A & AAAI 2020 \\ DDAG [1] & 69.34 & 63.46 & 68.06 & 61.80 & ECCV 2020 \\ Hi-CMD [25] & 70.93 & 66.04 & N/A & N/A & CVPR 2020 \\ cm-SSFT [15] & 72.3 & 72.9 & 71.0 & 71.7 & CVPR 2020 \\ MACE [37] & 72.37 & 69.09 & 72.12 & 68.57 & TIP 2020 \\ LADN [14] & 75.7 & 72.9 & 75.3 & 73.0 & IoT 2020 \\ AGW [12] & 70.05 & 66.37 & N/A & N/A & N/A & TPAMI 2021 \\ ADCNet [24] & 72.9 & 66.5 & 72.4 & 65.3 & ICME 2021 \\ FBP-AL [17] & 73.98 & 68.24 & 70.05 & 66.61 & TNNLS 2021 \\ LLM [30] & 74.85 & 71.32 & N/A & N/A & SPI 2021 \\ SFANet [19] & 76.31 & 68.00 & 70.15 & 63.77 & TNNLS 2021 \\ GECNet [22] & 82.33 & 78.45 & 78.93 & 75.58 & TCSVT 2021 \\ MPANet [46] & 83.7 & 80.9 & 82.8 & 80.7 & CVPR 2021 \\ MSA [21] & 84.86 & 82.16 & N/A & N/A & IJCAI 2021 \\ HC-Triplet [5] & 91.05 & 83.28 & 89.30 & 81.46 & TMM 2021 \\ GLMC [16] & 91.84 & 81.42 & 91.12 & 81.06 & TNNLS 2021 \\ DMiR [45] & 75.79 & 69.97 & 73.93 & 68.22 & TCSVT 2022 \\ DTRM [34] & 79.09 & 70.09 & 78.02 & 69.56 & TIFS 2022 \\ DFLN-ViT [35] & 92.10 & 82.11 & **91.21** & 81.62 & TMM 2022 \\ \hline MMFSD & **92.19** & **85.95** & 90.65 & **84.38** & Ours \\ \hline \end{tabular}
\end{table} 
Table 2: The performance comparison on RegDB dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{\(\lambda\)} & \multicolumn{3}{c}{**All Search**} & \multicolumn{3}{c}{**Indoor Search**} \\ \cline{2-7}  & **Rank1 (\%)** & **mAP (\%)** & **mINP (\%)** & **Rank1 (\%)** & **mAP (\%)** & **mINP (\%)** \\ \hline
0 & 56.04 & 54.71 & 41.96 & 59.95 & 67.23 & 63.35 \\
0.25 & **60.64** & **57.54** & **43.31** & 62.93 & **69.27** & **64.89** \\
0.5 & 57.96 & 54.64 & 40.15 & 61.87 & 67.66 & 62.82 \\
1 & 59.52 & 55.97 & 41.31 & **63.00** & 69.08 & 64.32 \\
2 & 55.27 & 51.42 & 35.66 & 54.21 & 61.92 & 57.09 \\ \hline \end{tabular}
\end{table} 
Table 3: The performance comparison of using different weights of MMFSD loss function (i.e., \(\lambda\) in Equation (9)) on the SYSU-MM01 dataset. On the SYSU-MM01 dataset, from Table 3 and Figure 3, it can be found that the proposed FSMD loss function achieves great performance. For example, compared with the case without FSMD loss function (i.e., \(\lambda=0\)), the Rank1, mAP and mINP obtain better results when \(\lambda=0.25\) on all-search mode (i.e., 60.64% Rank1, 57.54% mAP, and 43.31% mINP). It demonstrates the effectiveness of the FSMD loss function. On the RegDB dataset, Table 4 and Figure 4 also show the effectiveness of the FSMD loss function. Specifically, when adjusting \(\lambda\) from 0.25 to 2, the Rank1, mAP, and mINP are higher than those as \(\lambda=0\) on both visible-to-infrared retrieval and infrared-to-visible retrieval. Among them, when \(\lambda=0.5\), it obtains the best performance, i.e., 92.19% Rank1, 85.95% mAP and 72.86% mINP on visible-to-infrared retrieval, and 90.65% Rank1, 84.38% mAP and 70.73% mINP on infrared-to-visible retrieval.