## 4 Experiments
### Multi-Level Feature Fusion  
In this section, we add the Multi-Level Feature Fusion (MFF) method to the Res-CNN, TA-CNN (TA-CNN_SORT), GA-CNN (Res-CNN_G8), and TGA-CNN (TA-CNN_G8) models, respectively. The parameters of the MFF are as shown in Table 1. The \(2\times 2\) convolution kernels are used to reduce the feature dimension, and the \(1\times 1\) convolution kernels are applied to adjust the number of feature channels. This can reduce the network parameters while preserving the features of each layer as much as possible. The MFF method can combine low-level visual features with high-level semantic information. The experimental results are shown in Table 5.  
As shown in Table 5, after the introduction of the MFF method, the four network structures improved in classification accuracies, which indicates that low-level visual features and high-level semantic information did have an impact on the classification results. Comparing the first four and the last four groups of the experiments (i.e., introduced the network before and after the grouping attention), the parameters of the last four groups decreased, but the time complexity increased accordingly. Meanwhile, the classification accuracies of the latter four groups increased significantly. The parameters of TA-CNN_MFF were up to \(16.44M\). Comparing TGA-CNN and GA-CNN_MFF, it can be found that the latter achieved higher classification accuracies under the condition of less parameters and lower time complexity. This illustrates that it is necessary to perform group attention operation in the advanced semantic layers of the network. The classification accuracies of TGA-CNN_MFF on Cars and Air datasets reached \(91.05\%\) and \(87.93\%\), respectively. This is because we took into account the overall and part attentions, as well as the high-level and low-level features. Its time complexity was also the highest, and the value of FPS was \(23\). Compared with Res-CNN, with the support of the TA-model, GA-model, and MFF method, the classification accuracy of TGA-CNN_MFF increased by \(6.76\%\) and \(5.78\%\) on the datasets Stanford Cars and FGVC-Aircraft, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Accuracy(\%)**} & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**FPS**} \\ \cline{2-2} \cline{4-5}  & **Cars** & & & \\ \hline TA-CNN & 85.22 & 83.01 & 15.78M & 36 \\ TA-CNN\_G4 & 89.13 & 86.55 & 15.07M & 27 \\ TA-CNN\_G8 & 90.32 & 87.47 & 14.55M & 23 \\ TA-CNN\_G12 & 89.04 & 86.82 & 14.38M & 20 \\ TA-CNN\_GX & 86.81 & 84.69 & 14.20M & 36 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The parameters and FPS of the two-level attentions and group attention residual networks, and the classification accuracies of these networks on the two datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Accuracy(\%)**} & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**FPS**} \\ \cline{2-2} \cline{4-5}  & **Cars** & & & \\ \hline Res-CNN & 84.29 & 82.15 & 14.09M & 40 \\ Res-CNN\_MFF & 84.95 & 82.63 & 14.77M & 36 \\ TA-CNN & 85.22 & 83.01 & 15.78M & 36 \\ TA-CNN\_MFF & 85.64 & 83.26 & 16.44M & 34 \\ GA-CNN & 89.51 & 87.06 & 12.89M & 24 \\ GA-CNN\_MFF & 90.41 & 87.49 & 13.01M & 24 \\ TGA-CNN & 90.32 & 87.47 & 14.55M & 23 \\ TGA-CNN\_MFF & 91.05 & 87.93 & 14.67M & 23 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The parameters and FPS of the residual networks with multi-level feature fusion (MFF), and the classification accuracies of these networks on the two datasets.