# Hierarchical Feature Aggregation from Body Parts for Misalignment Robust Person Re-Identification +
## 4 Experiments
### Performance Evaluation
#### 4.3.3 Comparison with More State-of-The-Arts  
We compared our DJL with the current state-of-the-art DL-based methods. For ease of comparison, those methods are summarised into two categories: Pose-irrelevant DL-based methods and Pose-relevant DL-based methods. Their results on Market-1501 and CUHK03 are shown in Tables 4 and 5. The proposed DJL-S structure achieved comparable Rank-1 accuracy among the \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**Market-1501**} \\ \cline{2-6}  & **Rank-1** & **Rank-5** & **Rank-10** & **Rank-20** & **mAP** \\ \hline AlexNet & 57.75 & 77.52 & 84.47 & 89.46 & 33.80 \\ Residual-50 & 72.42 & 86.49 & 91.03 & 94.42 & 48.01 \\ InceptionNet & 79.66 & 91.51 & 94.54 & 96.50 & 56.59 \\ Proposed (AlexNet) & 67.64 & 84.80 & 89.88 & 93.53 & 43.60 \\ Proposed (Residual-50) & 78.86 & 90.38 & 93.91 & 96.35 & 55.49 \\ Proposed (InceptionNet) & **85.12** & **93.91** & **95.69** & **97.51** & **64.82** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison with the three base networks, AlexNet, Residual-50 and InceptionNet on Market-1501 (by adopting the proposed DJL-S structure) in terms of identification accuracy (%) and mAP (%). \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**CUHK03**} \\ \cline{2-5}  & **Rank-1** & **Rank-5** & **Rank-10** & **Rank-20** \\ \hline AlexNet & 53.03 & 79.53 & 87.82 & 94.21 \\ Residual-50 & 61.79 & 85.46 & 92.31 & 97.86 \\ InceptionNet & 80.85 & 95.90 & 98.17 & 99.48 \\ Proposed (AlexNet) & 71.95 & 90.30 & 94.91 & 98.16 \\ Proposed (Residual-50) & 80.83 & 95.92 & 98.66 & 99.54 \\ Proposed (InceptionNet) & **84.25** & **97.40** & **98.86** & **99.67** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with the three base networks, AlexNet, Residual-50 and InceptionNet on CUHK03 (by adopting the proposed DJL-S structure) in terms of identification accuracy (%). \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Base Network**} & \multicolumn{2}{c}{**Market-1501**} & \multicolumn{2}{c}{**CUHK03**} \\ \cline{2-5}  & **DJL-S** & **PIE** & **DJL-S** & **PIE** \\ \hline AlexNet & **+9.89** & +9.12 & **+18.92** & +2.65 \\ Residual-50 & **+6.44** & +5.66 & **+19.04** & +5.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Rank-1 accuracy improvement (%) over base networks compared with the PIE method. methods, i.e., 85.12% and 84.25% on Market-1501 and CUHK03, respectively. When adopting DJL-HFS structure and combining other re-ranking method (RK) [41], the performance was further boosted, reaching 88.39% on Market-1501. Furthermore, our Deep Joint Learning pipeline can be easily integrated with other state-of-the-art person re-ID networks. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Methods** & **Rank-1** & **mAP** \\ \hline Pose-irrelevant DL-based Methods & & \\ \hline APR [38] & 84.29 & 64.67 \\ DLCE [49] & 79.51 & 59.87 \\ DML [42] & 87.73 & 68.83 \\ Gate-SCNN [7] & 65.88 & 39.55 \\ JLML [51] & 85.10 & 65.50 \\ X-Corr [61] & - & - \\ \hline Ours & & \\ \hline DJL-S & 85.12 & 64.82 \\ DJL-HFA & 85.99 & 65.65 \\ DJL-HFA(RK) & 88.39 & **79.97** \\ \hline Pose-relevant DL-based Methods & & \\ \hline DLPA [53] & 81.0 & 63.4 \\ MSCAN [62] & 80.31 & 57.53 \\ PABP [23] & **88.8** & **74.5** \\ PDC [52] & 84.14 & 63.41 \\ PIE [19] & 78.65 & 53.87 \\ PIE + KISSME [19] & 79.33 & 55.95 \\ Spindle [22] & 76.90 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison with state-of-the-arts on Market-1501. Rank-1 accuracy (%) and mAP (%) are shown. The best result is marked in bold while the second best in gray.