# Cross-Camera Erased Feature Learning for Unsupervised Person Re-Identification
## 4 Results
### Comparison with the State-of-the-Art  
CEFL is experimented on Market-1501 [6] and DukeMTMC-reID [17], and experimental results are compared with other unsupervised person re-identification methods. Comparison results are shown in the following tables: Table 1 shows the comparison results on the Market-1501 [6] dataset, and Table 2 shows the comparison results on the DukeMTMC-reID [17] dataset.  
Compared with manual labeling methods (LOMO [5], BoW [6], and UMDL [10]), performance of our method is beyond these methods. This is because CEFL model can learn robust features. Our proposed method outperforms clustering-based methods (PUL [29], CAMEL [30], DECAMEL [31], and BUC [32]) significantly on both datasets. Clustering-based methods mainly identify pedestrians by assigning the same pseudo-labels to similar pedestrian images. But these similar pedestrian images may have different identities, which affects the discrimination of pedestrians. Other details in the erased feature map are used for matching, in CEFL, which strengthens the ability of feature discrimination.  
Compared with transfer learning methods (PTGAN [7], SPGAN + LMP [9], TJ-AIDJ [8], HHL [11], MAR [33], and PAUL [34]), our proposed method outperforms these methods. Due to the large gap between different datasets, it is still difficult to apply the model to the target domain. The CEFL model performs feature discrimination learning by extracting the global features and the erased features. Then the similarity measurement method can strengthen the generalization ability between different unlabeled datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**DukeMTMC**} \\ \cline{2-5}  & **Rank-1** & **Rank-5** & **Rank-10** & **mAP** \\ \hline LOMO [5] & 12.3 & 21.3 & 26.6 & 4.8 \\ BoW [6] & 17.1 & 28.8 & 34.9 & 8.3 \\ UMDL [10] & 18.5 & 31.4 & 37.6 & 7.3 \\ \hline PUL [29] & 30.0 & 43.4 & 48.5 & 16.4 \\ \hline PTGAN [7] & 27.4 & – & 50.7 & – \\ SPGAN+LMP [9] & 46.4 & 62.3 & 68.0 & 26.2 \\ TJ-AIDJ [8] & 44.3 & 59.6 & 65.0 & 23.0 \\ HHL [11] & 46.9 & 61.0 & 66.7 & 27.2 \\ BUC [32] & 47.4 & 62.6 & 68.4 & 27.5 \\ MAR [33] & 67.1 & 79.8 & – & 48.0 \\ PAUL [34] & 72.0 & 82.7 & 86.0 & 53.2 \\ \hline EFDL (Ours) & 73.1 & 83.7 & 86.9 & 55.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with existing methods on DukeMTMC-reID.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Market1501**} \\ \cline{2-5}  & **Rank-1** & **Rank-5** & **Rank-10** & **mAP** \\ \hline LOMO [5] & 27.2 & 41.6 & 49.1 & 8.0 \\ BoW [6] & 35.8 & 52.4 & 60.3 & 14.8 \\ UMDL [10] & 34.5 & 52.6 & 59.6 & 12.4 \\ \hline PUL [29] & 45.5 & 60.7 & 66.7 & 20.5 \\ CAMEL [30] & 54.5 & – & – & 26.3 \\ DECAMEL [31] & 60.2 & 76.0 & 81.1 & 32.4 \\ \hline PTGAN [7] & 38.6 & – & 66.1 & – \\ SPGAN+LMP [9] & 57.7 & 75.8 & 82.4 & 26.7 \\ TJ-AIDJ [8] & 58.2 & 74.8 & 81.1 & 26.5 \\ HHL [11] & 62.2 & 78.8 & 84.0 & 31.4 \\ BUC [32] & 66.2 & 79.6 & 84.5 & 38.3 \\ MAR [33] & 67.7 & 81.9 & – & 40.0 \\ PAUL [34] & 68.5 & 82.4 & 87.4 & 40.1 \\ \hline EFDL (Ours) & 74.4 & 85.5 & 88.9 & 47.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with existing methods on Market1501.