# DMA-Net: Decoupled Multi-Scale Attention for Few-Shot Object Detection
## 4 Experiments and Discussions
### Incremental FSOD Protocol  
In this section, we describe our evaluation of the DMA-Net in a more challenging situation. We directly tested the proposed model on novel classes after the network was trained on base classes without any novel samples for fine-tuning. Therefore, we only needed to prepare K bounding boxes of each novel class as support data to recognize objects of that class. In this setting, the network was able to recognize unseen novel classes at any time, which is referred to as incremental FSOD. In Table 2, the studies that we compared, including the proposed method, the data input, and result evaluation, showed the same result, and there was no ambiguity in comparison. Table 2 shows that the DMA-Net outperformed the SOTA method DAnA-FasterRCNN [35] by 0.5/0.8/0.1 mAP according to the AP/AP\({}_{50}\)/AP\({}_{75}\) metrics. An excellent FSOD network should also achieve good performance with base classes. Table 2 shows a comparison between FSOD methods and the original Faster R-CNN in terms of accuracy with base classes [3]. FGN [57] and attention RPN had a large gap with Faster R-CNN [3], while the performance of the DMA-Net is nearer to that of Faster R-CNN [3]. Therefore, the proposed network is able improve accuracy with novel classes without losing accuracy with base classes, and DMA-Net can achieve a good performance on AP/AP\({}_{50}\)/AP\({}_{75}\) metrics of both novel and base categories. In addition, DAnA [35] was trained for 16 epochs on COCO datasets, while the proposed network had fewer parameters and only required half of the epochs to reach SOTA accuracy; thus, DMA-Net also reduces additional costs compared with the methods used in previous studies. DAnA [35] evaluated models with different numbers of support images under 2-way 3-shot training, including the case of 1 shot and 5 shots. In Table 3, we show the incremental results for the novel and base classes of COCO with different numbers of support images. The proposed model performed best in the case of 1 shot, as well as the case of 3 shots, but performed less well in the case of 5 shots. The proposed model improved markedly with a decrease in the number of support images, which demonstrates that DMA-Net is more suitable for the few-shot case, but loses its generalizability as the number of support images increases. This result likely occurs because we only chose the average features of their prototypes when processing support images; therefore, how to better extract the features of support images should be investigated in more detail. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Novel Categories**} & \multicolumn{4}{c}{**Base Categories**} & \multirow{2}{*}{**Parameters**} \\ \cline{2-2} \cline{4-9}  & **AP** & & **AP\({}_{50}\)** & & **AP\({}_{75}\)** & & **AP** & **AP\({}_{50}\)** & **AP\({}_{75}\)** \\ \hline Faster R-CNN [3] & N/A & N/A & N/A & 34.3 & 58.3 & 35.6 & 4.76 \(\times\) 10\({}^{7}\) \\ Meta R-CNN [25] & \(11.1\) & 25.3 & 8.5 & 28.6 & 52.5 & 28.4 & 4.76 \(\times\) 10\({}^{7}\) \\ FGN [57] & \(10.5\) & 22.5 & 8.8 & 25.5 & 46.4 & 25.5 & 1.48 \(\times\) 10\({}^{8}\) \\ Attention RPN [32] & \(10.1\) & 23.0 & 8.3 & 22.4 & 40.8 & 22.2 & 1.03 \(\times\) 10\({}^{8}\) \\ DAnA-FasterRCNN [35] & \(14.0\) & 28.9 & 13.0 & 29.4 & 50.6 & 30.3 & 1.42 \(\times\) 10\({}^{8}\) \\ DMA-Net & \(14.5\) & 29.7 & 13.1 & 30.1 & 53.0 & 31.2 & 6.59 \(\times\) 10\({}^{7}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Incremental results for novel and base classes of COCO. Faster R-CNN [3] is trained and evaluated under generic object detection protocol, so the results on novel categories are not applicable.