# Learning a Hierarchical Global Attention for Image Classification
## 5 Experiments
### Ablation Study  
**Investigation on different modules**. To demonstrate the effectiveness of three different modules, we rebuild the HGA with different module combinations. The results are shown in Table 5. From the table, we can find that MFE plays as a critical role for performance improvement. On one hand, MFE extracts the multi-scale information from input features which provides a diverse comprehension. On the other hand, the MFE module increases the network depth, which helps to enhance the representation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Model**} & **ResNet-50** & **ResNet-101** & **ResNet-152** & **ResNeXt-50** & **ResNeXt-101** \\ \hline \multirow{2}{*}{**Vanilla**} & **Top 1 err.(\%)** & 24.80 & 23.17 & 22.42 & 22.11 & 21.18 \\ \hline \multirow{2}{*}{**SENet**[7]} & **Top 1 err.(\%)** & 23.29 (\(-1.51\)) & 22.38 (\(-0.79\)) & 21.57 (\(-0.85\)) & 21.10 (\(-1.01\)) & 20.70 (\(-0.48\)) \\  & **FLOPs(G)** & 4.11 & 7.83 & 11.56 & 4.26 & 8.01 \\ \hline \multirow{2}{*}{**SKNet**[35]} & **Top 1 err.(\%)** & - & - & - & 20.79 (\(-1.23\)) & 20.19 (\(-0.84\)) \\  & **FLOPs(G)** & - & - & - & 4.47 & 8.46 \\ \hline \multirow{2}{*}{**HGA**} & **Top 1 err.(\%)** & 22.48 (\(-2.32\)) & 21.50 (\(-1.67\)) & 20.63 (\(-1.79\)) & 20.22 (\(-1.89\)) & 19.82 (\(-1.36\)) \\  & **FLOPs(G)** & 4.38 & 8.23 & 12.13 & 4.53 & 8.71 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparisons of different attention mechanism on ImageNet dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Model**} & **ResNet-50** & **ResNet-101** & **ResNet-152** & **ResNeXt-50** & **ResNeXt-101** \\ \hline \multirow{4}{*}{**Vanilla**} & **Top 1 err.(\%)** & 24.80 & 23.17 & 22.42 & 22.11 & 21.18 \\  & **Top 5 err.(\%)** & 7.48 & 6.52 & 6.34 & 5.90 & 5.57 \\ \cline{2-6}  & **FLOPs(G)** & 4.11 & 7.83 & 11.55 & 4.25 & 8.01 \\  & **Params(M)** & 25.55 & 44.54 & 60.19 & 25.02 & 44.17 \\ \hline \multirow{4}{*}{**HGA**} & **Top 1 err.(\%)** & 22.48 (\(-2.32\)) & 21.50 (\(-1.67\)) & 20.63 (\(-1.79\)) & 20.22 (\(-1.89\)) & 19.82 (\(-1.36\)) \\  & **Top 5 err.(\%)** & 6.22 (\(-1.26\)) & 5.60 (\(-0.92\)) & 5.23 (\(-1.11\)) & 4.88 (\(-1.02\)) & 4.59 (\(-0.98\)) \\ \cline{1-1} \cline{2-6}  & **FLOPs(G)** & 4.38 & 8.23 & 12.13 & 4.53 & 8.71 \\ \cline{1-1}  & **Params(M)** & 26.73 & 47.07 & 63.92 & 26.21 & 46.70 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Single-crop error rates of different models on ImageNet dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**MFE**} & **HGA** & **EE** & \multicolumn{2}{c}{**ResNet-101**} & \multicolumn{2}{c}{**ResNeXt-101**} \\  & **Top 1 err.(\%)** & **Top 5 err.(\%)** & **Top 1 err.(\%)** & **Top 1 err.(\%)** & **Top 5 err.(\%)** \\ \hline ✓ & ✓ & ✓ & 21.50 & 5.60 & 19.82 & 4.59 \\ ✗ & ✓ & ✓ & 21.91 & 5.73 & 20.22 & 4.81 \\ ✓ & ✗ & ✓ & 21.58 & 5.63 & 19.92 & 4.62 \\ ✓ & ✓ & ✗ & 21.63 & 5.69 & 20.01 & 4.69 \\ ✓ & ✗ & ✗ & 21.85 & 5.69 & 20.03 & 4.63 \\ ✗ & ✓ & ✗ & 22.40 & 6.20 & 20.48 & 5.09 \\ ✗ & ✗ & ✓ & 22.00 & 5.75 & 20.34 & 4.88 \\ ✗ & ✗ & ✗ & 23.17 & 6.52 & 21.18 & 5.57 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparisons of different module combinations.  
**Investigation on HGA module.** To further explore the capacity of HGA module, we modify the multi-scale extraction with different convolutional layers, which are shown in Table 6. From the result, we can find that the larger filters will help more on the accuracy improvement. Different filter sizes will exploit different scale information, and the HGA module can comprehensively exploit the multi-scale feature for better representation and semantic understanding.