# Target State Classification by Attention-Based Branch Expansion Network
## 4 Experiments
### Comparison of Attention-Based Branch Expansion Structures  
In the following experiment, we conduct a longitudinal comparison of the four \"ABE Depth-Width\" models and a horizontal comparison of the \"XC\" and \"ABE\" models in pairs with the same structure. First, the position and quantity of the attention module vary with the structure of the model. Therefore, even if the model \"XC 1-8\" with a one-block, eight-branch structure performs best, as mentioned above, it is not certain that the corresponding attention model \"ABE 1-8\" is the best. It is necessary to conduct detailed ablation studies on the attention-based models of the four structures. Table 5 consists of two parts, the front four rows are the performance of the four models without attention, which are listed here to facilitate horizontal comparisons between the models with the same structure, and the last four rows list the results of the four models after adopting the attention mechanism. It can be found that model \"ABE 2-4\" performs best on all the three datasets indicated in bold in row 7. We also found that using the attention mechanism does not necessarily improve performance, and may even result in a decline, such as \"ABE 8-1\" decreased by 3%, compared to the Xception model on the \"wearing-state\" dataset. Coincidentally, the \"ABE 1-8\" model, after adding the attention module to \"XC 1-8\", which performs best among the four attention-free models, decreases on all three datasets and performs worst among the four attention-added models. For structure 2, in contrast to \"ABE 4-2\" and \"XC 4-2\", the performance is slightly improved after using the attention module. Therefore, for the deepest original Xception and the widest network \"XC 1-8\", there is no need to use the attention mechanism. For \"XC 2-4\", with 2 stacked blocks, each consisting of 4 parallel branches, the attention module after each block does refine features extracted by the block with an improvement of about 3-4%. The best attention-added model \"ABE 2-4\" is about 2% higher than the best attention-free model \"XC 1-8\" on the three datasets. This conclusion is further illustrated in the visualization results in the next sub-section. The above conclusions explain that the performance of the linear stacked block models with different widths and depths is not consistent before and after adding block attention modules. The challenge datasets with relatively few images and few key features call for widening the network, instead of deepening it. While using the attention mechanism to \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Object-State** & **Wearing-State** & **Trashnet** \\ \hline Xception & 73.44 & 83.11 & 91.41 \\ XC 4-2 & 73.09 & 83.59 & 90.62 \\ XC 2-4 & 75.78 & 84.38 & 92.58 \\
**XC 1-8** & **77.34** & **86.00** & **93.36** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the performance (%) between the 4 structures without attention on our proposed two datasets and one common dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Object-State** & **Wearing-State** & **Trashnet** \\ \hline Xception & 73.44 & 83.11 & 91.41 \\ XC 4-2 & 73.09 & 83.59 & 90.62 \\ XC 2-4 & 75.78 & 84.38 & 92.58 \\
**XC 1-8** & **77.34** & **86.00** & **93.36** \\ \hline ABE 8-1 & 73.24 & 80.03 & 91.34 \\ ABE 4-2 & 76.56 & 83.43 & 91.88 \\
**ABE 2-4** & **79.69** & **88.28** & **94.53** \\ ABE 1-8 & 71.09 & 82.79 & 91.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Longitudinal comparison of the 4 “ABE Depth-Width” models and horizontal comparison of models in pairs with the same structure on the 3 datasets. further refine features, we need to balance the depth and width of the network to achieve the perfect balance with the attention module. We verify the above conclusions through visualization results in the next part and exhibit the parameters and computation of the eight networks on the Trashnet dataset to illustrate the additional overhead of parameters and computation with the addition of attention modules.