# An Efficient Multi-Scale Focusing Attention Network for Person Re-Identification
## Results on Feature learning of MSFANet  
We conduct a set of feature map visualizations on the final output feature maps of MSFANet with attention, MSFANet without attention, OSNet, MobileNet v2, and ResNet50 with attention, as shown in Figure 7. We notice that MSFANet without attention has no apparent attention to region features, while MSFANet with attention can better focus on the regional features, as shown in the 1 st and 2 nd columns of the following figure. However, the attention regions can sometimes overly emphasize some local areas (e.g., clothes), implying the risk of overfitting person-irrelevant nuisances. In contrast, the attention of MSFANet can strike a better balance: it focuses on more of the local parts of the person's body while still being able to eliminate the person from backgrounds. Compared with other models, such as OSNet, MobileNet v2, and ResNet50, MSFANet's multi-scale attention has a more preeminent ability in extracting discriminative ReID features from the row images. MobileNetV2 and ResNet50 are more about capturing global features and ignoring significant local features. The results show that MSFANet performs state-of-the-art. **Results on the inference of MSFANet. ** In the inference stage, given a query image, MSFANet can find the same ID in the gallery in a short time. The gallery can be a messy image collection or a series of video sequences. As shown in Figure 8, we test some samples on MSFANet. It can be observed that no matter for simple samples (1), (2), (3), (5), or for difficult samples (4), MSFANet can return the correct match (if the query in gallery). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Publication**} & \multirow{2}{*}{**Backbone**} & \multicolumn{2}{c}{**Market1501**} & \multicolumn{2}{c}{**DukeMTMC**} & \multicolumn{2}{c}{**Complexity**} \\ \cline{3-10}  & & & **Rank-1** & **mAP** & **Rank-1** & **mAP** & **Param (10\({}^{\ddagger}\))** & **GFLOPs** \\ \hline \multicolumn{8}{c}{**Without ImageNet Pre-training**} \\ \hline \(\dagger\) ShuffleNet [15] & CVPR’18 & ShuffleNet & 84.8 & 65 & 71.6 & 49.9 & 5.34 & 0.38 \\ \(\dagger\) MobileNetV2 [14] & CVPR’18 & MobileNetV2 & 87 & 69.5 & 75.2 & 55.8 & 4.32 & 0.44 \\ \(\dagger\) BraidNet [16] & CVPR’18 & BraidNet & 83.7 & 69.5 & 76.4 & 59.5 & - & - \\ \(\dagger\) HAN [25] & CVPR’18 & Inception & 91.2 & 75.7 & 80.5 & 63.8 & 4.52 & 0.53 \\ \(\dagger\) PCB [24] & ECCV’18 & ResNet & 92.4 & 77.3 & 81.9 & 65.3 & 23.53 & 2.74 \\ \(\dagger\) OSNet [27] & ICCV’19 & OSNet & **93.6** & 81 & **84.7** & 68.6 & **2.23** & 0.98 \\ \hline \(\dagger\)\# MSFANet + \(\uparrow\)2-3’ (ours) & - & MSFANet & 92.5 & **82.5** & 82.6 & **69.4** & 2.52 & **0.82** \\ \hline \(\dagger\)\# MSFANet + 2-2-2’ (ours) & - & MSFANet & 92.9 & **82.3** & 82.8 & **69.5** & 2.25 & **0.94** \\ \hline \multicolumn{8}{c}{**With ImageNet Pre-training**} \\ \hline DaRe [4] & CVPR’18 & DenseNet & 89 & 76 & 80.2 & 64.5 & 6.95 & 1.85 \\ MLFN [26] & CVPR’18 & ResNeXt & 90 & 74.3 & 81 & 62.8 & 22.98 & 2.76 \\ \(\dagger\) Bilinear [51] & ECCV’18 & Inception & 91.7 & 79.6 & 84.4 & 69.3 & 4.5 & 0.5 \\ G2G [52] & CVPR’18 & ResNet & 92.7 & 82.5 & 80.7 & 66.4 & 23.5 & 2.7 \\ DeepCRF [53] & CVPR’18 & ResNet & 93.5 & 81.6 & 84.9 & 69.5 & 23.5 & 2.7 \\ IANet [54] & CVPR’19 & ResNet & 94.4 & 84.5 & 87.1 & 73.4 & 23.5 & 2.7 \\ DGNet [55] & CVPR’19 & ResNet & 94.8 & 84.5 & 86.6 & 74.8 & 23.5 & 2.7 \\ OSNet [27] & ICCV’19 & OSNet & **94.8** & 84.5 & **88.6** & 73.5 & **2.2** & 0.98 \\ \hline \(\ddagger\) MSFANet + 1-2-3’ (ours) & - & MSFANet & 94.3 & **86.8** & 86.8 & **76.6** & 2.52 & **0.82** \\ \hline \(\ddagger\) MSFANet + 2-2-2’ (ours) & - & MSFANet & 94.5 & **86.9** & 87.0 & **76.3** & 2.25 & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results (%) on big ReID datasets. The top half of the table shows the performance of MSFANet without ImageNet pre-training, compared with other state-of-the-art methods, while the bottom half are the results based on ImageNet pre-training.