# MFCNet: Mining Features Context Network for RGB-IR Person Re-Identification
## 5 Discussion  
In this section, the effectiveness of each component of the network designed in this paper is verified based on ablation experiments. We also experimentally verified the improvement of the network by embedding different positions and numbers of SGA  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Settings** & \multicolumn{4}{c}{**All-Search**} & \multicolumn{4}{c}{**Indoor Search**} \\ \hline
**Methods** & **r = 1** & **r = 10** & **r = 20** & **mAP** & **r = 1** & **r = 10** & **r = 20** & **mAP** \\ \hline One-stream [9] & 12.04 & 49.68 & 66.74 & 13.67 & 16.94 & 63.55 & 82.1 & 22.95 \\ \hline Two-stream [9] & 11.65 & 47.99 & 65.5 & 12.85 & 15.6 & 61.18 & 81.02 & 21.49 \\ \hline Zero-padding [9] & 14.8 & 54.12 & 71.33 & 15.95 & 20.58 & 68.38 & 85.79 & 26.92 \\ \hline Tone [46] & 12.52 & 50.72 & 68.6 & 14.42 & 20.82 & 68.86 & 84.46 & 26.38 \\ \hline HCML [46] & 14.32 & 53.16 & 69.17 & 16.16 & 24.52 & 73.25 & 86.73 & 30.08 \\ \hline cmGAN [33] & 26.97 & 67.51 & 80.56 & 31.49 & 31.63 & 77.23 & 89.18 & 42.19 \\ \hline BDTR [24] & 27.32 & 66.96 & 81.07 & 27.32 & 31.92 & 77.18 & 89.28 & 41.86 \\ \hline HSME [25] & 20.68 & 32.74 & 77.95 & 23.12 & - & - & - & - \\ \hline D2 RL [35] & 28.9 & 70.6 & 82.4 & 29.2 & - & - & - & - \\ \hline MAC [47] & 33.26 & 79.04 & 90.09 & 36.22 & 36.43 & 62.36 & 71.63 & 37.03 \\ \hline MSR [29] & 37.35 & 83.4 & 93.34 & 38.11 & 39.64 & 89.29 & 97.66 & 50.88 \\ \hline AlignGAN [34] & 42.4 & 85 & 93.7 & 40.7 & 45.9 & 87.6 & 94.4 & 54.3 \\ \hline HPLIN [27] & 41.36 & 84.78 & 94.31 & 42.95 & 45.77 & 91.82 & **98.46** & 56.52 \\ \hline AGW [15] & 47.5 & 84.39 & 92.14 & 47.65 & 54.17 & 91.14 & 95.98 & 62.97 \\ \hline LCRF [48] & 43.23 & 82.78 & 90.91 & 43.09 & 50.07 & 90.63 & 96.99 & 58.88 \\ \hline H2H [49] & 45.47 & 72.78 & 82.28 & 47.99 & - & - & - & - \\ \hline FBP-AL [50] & **54.14** & 86.04 & 93.03 & **50.20** & - & - & - & - \\ \hline Ours & 51.64 & **86.8** & **94.66** & 49.99 & **57.29** & **92.89** & 96.38 & **64.91** \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Settings** & \multicolumn{4}{c}{**Visible to Infrared**} \\ \hline
**Methods** & **r = 1** & **r = 10** & **r = 20** & **mAP** \\ \hline HOG [45] & 13.49 & 33.22 & 43.66 & 10.31 \\ \hline LOMO [3] & 0.85 & 2.47 & 4.10 & 2.28 \\ \hline Zero padding [9] & 17.75 & 34.21 & 44.35 & 18.90 \\ \hline HCML [46] & 24.44 & 47.53 & 56.78 & 20.08 \\ \hline BDTR [24] & 33.56 & 58.61 & 67.43 & 32.76 \\ \hline HSME [25] & 50.85 & 73.36 & 81.66 & 47.00 \\ \hline D2 RL [35] & 43.4 & 66.1 & 76.3 & 44.1 \\ \hline MAC [47] & 36.43 & 62.36 & 71.63 & 37.03 \\ \hline MSR [29] & 48.43 & 70.32 & 79.95 & 48.67 \\ \hline EDFL [26] & 52.58 & 72.10 & 81.47 & 52.98 \\ \hline AlignGAN [34] & 57.9 & - & - & 53.6 \\ \hline Xmodal [31] & 62.21 & 83.13 & 91.72 & 60.18 \\ \hline HZH [49] & 62.27 & 77.56 & 83.72 & **61.90** \\ \hline Ours & **69.76** & **86.07** & **91.73** & 52.52 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the results of the proposed method and other state-of-the-art methods based on the RegDB dataset. Bold numbers are the best.  
modules. It proves that the network proposed in this paper can extract more discriminative features and is optimal.