# DMA-Net: Decoupled Multi-Scale Attention for Few-Shot Object Detection
## 4 Experiments and Discussions
### Ablations
#### 4.5.1 Effectiveness of Layer-to-Layer Attention  
The few shots of the novel class make the information of this class valuable; therefore, we hope to make the best use of the information about these novel objects when recognizing objects. Using the features of the query image with different scales can improve the use of query information. We followed DAnA [35] to evaluate the proposed model on the COCO benchmark, in which there are many small objects. It is also a critical point for FSOD models to improve their ability to detect small objects; thus, we applied a feature pyramid in the query branch for multi-scale feature extraction. Ablation (a,b) in Table 4 shows that multi-scale feature extraction is more effective than single-feature extraction. In Figure 9, the visualization of the multi-scale features before RPN is shown. As the level of feature layers increased, attention was shifted from small objects to large objects, and we obtained the common effect of these layers concurrently using multi-scale features. Therefore, when we discovered large, easy-to-detect objects, we were also able to consider detecting small objects that were more difficult to detect. As shown in Figure 10, the model using multi-scale features for detection had a stronger recognition ability for small objects. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \begin{tabular}{c} **Multi-Scale** \\ **Feature** \\ \end{tabular} & \begin{tabular}{c} **Scale-to-Scale** \\ **Attention** \\ \end{tabular} & \begin{tabular}{c} **Decoupled** \\ **Gradient** \\ \end{tabular} & **AP** & \
\begin{tabular}{c} **AP\({}_{50}\)** \\ \end{tabular} &
\begin{tabular}{c} **AP\({}_{75}\)** \\ \end{tabular} \\ \hline a & & & & & 11.1 & 24.6 & 8.5 \\ b & ✓ & & & 11.7 & 24.7 & 10.3 \\ c & ✓ & ✓ & & 12.5 & 26.9 & 10.9 \\ d & ✓ & ✓ & ✓ & 14.5 & 29.7 & 13.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Effectiveness of each module. Figure 9: Visualization of the multi-scale features. The deeper the color is, the more the model responded. Each column represents the feature at level P2, P4, and P6, respectively. Adding support information into query features is a key point in FSOD models. The models in [24; 25] did not fuse support information with query features before RPN, and the models in [35; 36] utilized complex fusion methods before RPN. In this study, we would have created two problems if we had added a fusion operation before RPN. First, the features of the query image are at different scales, while the features of support images are single-scale, and there is a scale problem when fusing features. We, thus, used the feature pyramid operation in the support branch to obtain features with scales corresponding to the query features and reduced the loss of information caused by direct scaling of the features. This facilitated the subsequent scale-to-scale feature fusion for features of the two branches. Second, the introduction of a feature pyramid increases the complexity of the model. Due to the rarity of novel classes, the complex fusion operations lead to overfitting of the model. In addition, the model is trained on base classes and evaluated on novel classes directly without fine-tuning, which causes the model to fit the base classes well and lose its generalizability for novel classes. We used the fusion operation described in Section 3.2.1-(1) layer to layer. Ablation, shown in (a,c) of Table 4, confirmed the effectiveness of this module. As shown in Table 5, we performed an ablation study of the feature fusion operation of two branches before RPN and determined what fusion operation would be used and where (Line e in Table 5 is the proposed final model result). First, the ablation result (a,e) in Table 5 shows that the fusion operation involved in the upsampling (in Figure 4) of query feature extraction allows the model to obtain better results. Second, we compared a simple fusion operation (Section 3.2.1-(2)) and a complex fusion operation (Channel attention module [58]), as shown in Table 5 (b,e).