# DMA-Net: Decoupled Multi-Scale Attention for Few-Shot Object Detection
## 4 Experiments and Discussions
### Discussion  
Based on the experimental results, the proposed network achieved comparable results for the base and novel classes in the COCO dataset with widely used baselines. Table 2 shows that the proposed network achieved a good performance on both the novel and base classes without fine-tuning. We provide many visualizations of the detection results of novel classes in the COCO dataset in Figure 12. We also used a multi-scale feature extractor in the query branch and integrated support information into the features before the RPN so that the RPN would be able to more clearly distinguish the foreground and background when rare samples are being evaluated. We proposed a layer-to-layer attention operation based on different scales to fuse the information between the support and query branch, which overcame the scale mismatch problem and avoided the overfitting caused by the increase in complexity. Gradient decoupling was performed between the two branches so that the property of the gradient and the parameters it updated could be unified. Regarding previous studies, only the support information was fused into the detection module in [25]; the support information was fused before the RPN with a single size in [28; 36]; and the gradient between the backbone, RPN, and RCNN was decoupled in [46]; on the other hand, our method effectively utilized the support information from the aspect of scale and determined the specific location and mode of fusion operation. In addition, we analyzed the contradicting difference between the proposed model and original Faster R-CNN [3] in Section 3.2.1-(3). We were obliged to decouple only the gradient between the support branch and the RPN, and the experimental results demonstrate the effectiveness of the proposed methods; thus, we did not require additional decoupling operations. Our model not only achieved strong performance on novel classes, but also maintained a comparable level of detection accuracy to the original Faster R-CNN on base classes. Compared with the SOTA model DAnA [35] in the same setting, DMA-Net was able to achieve a better performance with fewer parameters and less training time without fine-tuning. However, in terms of detection efficiency, DMA-Net and DAnA [35] achieved FPS values of 7.28 and 9.45, respectively, in the same hardware environment. Although the gap was small, our model faced a disadvantage due to the utilization of multi-scale features during the extraction of information from two branches, leading to a significant increase in computational requirements. Therefore, reducing the computational burden of the proposed model should be a focus of future investigations. In addition, we have not yet solved the problem of how to use the proposed network to fuse category-agnostic information effectively by improving the extraction operation of the support features and the fusion operation between two branches. We will attempt to improve the proposed method in this regard in future work.  
Figure 12: Visualization of detection results of novel classes in the COCO dataset (incremental results with 3 support images).