# Unsupervised Domain Adaptive Person Re-Identification via Intermediate Domains
## 4 Experimental Results and Discussion
### Comparison with Other Methods  
The results of the proposed method are compared with those of other SOTA methods of UDA in this section. The performance is very competitive, as illustrated in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**DukeMTMC-reID\(\rightarrow\)Market1501**} & \multicolumn{3}{c}{**Market1501\(\rightarrow\)DukeMTMC-reID**} \\ \cline{3-10}  & & **mAP** & **Rank-1** & **Rank-5** & **Rank-10** & **mAP** & **Rank-1** & **Rank-5** & **Rank-10** \\ \hline ECN [32] & CVPR’19 & 43.0 & 75.1 & 87.6 & 91.6 & 40.4 & 63.3 & 75.8 & 80.4 \\ CR-GAN [33] & ICCV’19 & 54.0 & 77.7 & 89.7 & 92.7 & 48.6 & 68.9 & 80.2 & 84.7 \\ SSG [34] & ICCV’19 & 58.3 & 80.0 & 90.0 & 92.4 & 53.4 & 73.0 & 80.6 & 83.2 \\ MMCL [35] & CVPR’20 & 60.4 & 84.4 & 92.8 & 95.0 & 51.4 & 72.4 & 82.9 & 85.0 \\ ACT [36] & AAAI’20 & 60.6 & 80.5 & - & - & 54.5 & 72.4 & - & - \\ SNR [37] & CVPR’20 & 61.7 & 82.8 & - & - & 58.1 & 76.3 & - & - \\ ECN++ [38] & TPAMI’20 & 63.8 & 84.1 & 92.8 & 95.4 & 54.4 & 74.0 & 83.7 & 87.4 \\ AD-Cluster [39] & CVPR’20 & 68.3 & 86.7 & 94.4 & 96.5 & 54.1 & 72.6 & 82.5 & 85.5 \\ DAAM [40] & AAAI’20 & 67.8 & 86.4 & - & - & 63.9 & 77.6 & - & - \\ NRMT [17] & ECCV’20 & 71.7 & 87.8 & 94.6 & 96.5 & 62.2 & 77.8 & 86.9 & 98.5 \\ MMT [5] & ICLR’20 & 72.1 & 87.7 & 94.9 & 96.9 & 65.1 & 78.0 & 88.8 & 92.5 \\ B-SNR+GDS-H [41] & ECCV’20 & 72.5 & 89.3 & - & - & 59.7 & 76.7 & - & - \\ MEB-Net [18] & ECCV’20 & 76.0 & 89.9 & 96.0 & 97.5 & 66.1 & 79.6 & 88.3 & 92.2 \\ SpCL [16] & NeurIPS’20 & 76.7 & 90.3 & 96.2 & 97.7 & 68.8 & **82.9** & 90.1 & 92.5 \\ HSR [42] & ICIP’21 & 65.2 & 85.3 & - & - & 58.1 & 76.1 & - & - \\ UNRN [6] & AAAI’21 & 78.1 & 91.9 & 96.1 & 97.8 & **69.1** & 82.0 & 90.7 & **93.5** \\ \hline Baseline & & 78.7 & 92.0 & 96.5 & 97.9 & 68.5 & 82.1 & 90.1 & 92.3 \\ Ours & **80.2** & **92.4** & **97.1** & **98.1** & **69.1** & 82.3 & **90.8** & 92.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of ablation experiments for Market1501 to MSMT17 dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **mAP** & **Rank-1** & **Rank-5** & **Rank-10** \\ \hline market(pre-train) & 83.2 & 93.9 & 97.7 & 98.5 \\ directly transfer & 6.9 & 19.4 & 28.7 & 33.9 \\ msmt2m\_0.2 & 12.3 & 30.1 & 40.7 & 46.1 \\ msmt2m\_0.5 & 19.0 & 42.6 & 53.5 & 59.0 \\ msmt2m\_0.8 & 22.8 & 48.3 & 59.8 & 65.0 \\ msmt17 & 27.6 & 55.5 & 66.8 & 71.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of ablation experiments for DukeMTMC-reID to MSMT17 dataset.  
We used the strong UDA baseline mentioned in Section 3. The performance of our baseline approaches even exceeds that of many SOTA methods. The accuracy of our proposed method is further improved with the help of intermediate domains. Our method significantly outperforms the second best UDA method by 2.1%, 2.3% and 3.0%, in mAP accuracy, for Duke\(\rightarrow\)Market, Market\(\rightarrow\)MSMT and Duke\(\rightarrow\)MSMT, respectively. Although the mAP accuracy of Duke\(\rightarrow\)Market is the same as that of UNRN [6], the rank-1 accuracy outperforms it by 0.3% with a plain ResNet-50 backbone. DAAM [40] introduces an attention module and incorporates domain alignment constraints. MMT [5] uses two networks (four models) and MEB-Net [18] utilizes three networks (six models) to perform mutual mean teacher training, which presents a high computation complexity in training. Our proposed method only utilizes three intermediate domains without requiring extra computing and hardware resources, but still outperforms other approaches.