# Study on Few-Shot Object Detection Approach Based on Improved RPN and Feature Aggregation
## 4 Experiment and Results Analysis
### Experimental Results and Analysis  
Firstly, this paper conducts comparative experiments on the IFA-FSOD algorithm using \(N\)-way \(K\)-shot conditions on the PASCAL VOC dataset, and evaluates it against previous FSOD methods such as TFA [6], Meta R-CNN [7], and MetaDet [31]. Here, \(K=1,2,3,5,10\), respectively. And according to the experimental setup of TFA, three types of classification results for base and new classes were sampled, namely Novel Set 1, 2 and 3. We evaluate using the AP50 predicted by the new class on the VOC-2007 test set [3]. The experimental results are shown in Table 1, which presents the evaluation results of three different segmentation methods on the VOC dataset. We can observe that the proposed method in this paper has achieved higher performance than other methods in most cases. In the \(K\)-shot task, when \(K=2\) and 3, the proposed method achieved better performance compared to other methods. In the 3-shot task of Novel Set 1, compared with the best performing QA-FewDet [19] algorithm among the comparison methods, the proposed method achieved nearly 3% improvement. For larger 5-shot and 10-shot, there is not much difference from QA-FewDet, and the results obtained by different partitioning methods are not the same. For example, in Novel Set 2, the detection performance of 5-shot and 10-shot is inferior to QA-FewDet, but in Novel Set 1, the detection performance of 5-shot and 10-shot is better than QA-FewDet. Table 2 shows the comparison of detection performance between IFA-FSOD and other methods on the relatively more challenging MSCOCO dataset for new categories. The average precision (AP) was evaluated using 10-shot and 30-shot methods. AP is calculated based on different IoU (threshold) sizes. That is, by calculating the average threshold between each predicted box and the true box to reflect the AP. AP50 represents the corresponding AP to an IoU value greater than or equal to 50%, and AP75 represents the corresponding AP to an IoU value greater than or equal to 75%. From the results in Table 2, compared to other model methods, IFA-FSOD achieved better performance at different \(K\) values. When \(K=10\), compared to the method \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**mAP50\% for Different VOC Methods**} \\ \hline \multirow{3}{*}{**Split**} & \multirow{3}{*}{**Shot**} & \multirow{3}{*}{**FSRW**} & **Meta** & **MetaDet** & **TFA** & **SRR** & **QA** & **DA** & **FSC** & **IFA-FSOD** \\  & & & IS1 & **R-CNN**[7] & [31] & [61] & **-FSD**[24] & **-FewDet**[19] & **-FSOD**[32] & [23] & **Ours**) \\ \hline \multirow{3}{*}{Set 1} & 1 & 14.8 & 19.9 & 18.9 & 39.8 & 47.8 & 42.4 & 33.4 & 32.9 & 30.1 \\  & 2 & 15.5 & 25.5 & 20.6 & 36.1 & 50.5 & 51.9 & 45.1 & 44.0 & 52.3 \\ \multirow{3}{*}{Set 1} & 3 & 26.7 & 35.0 & 30.2 & 44.7 & 51.3 & 58.7 & 47.1 & 46.8 & 58.7 \\  & 5 & 33.9 & 45.7 & 36.8 & 55.7 & 55.2 & 62.6 & 53.1 & 52.9 & 62.4 \\  & 10 & 47.2 & 51.5 & 49.6 & 56.0 & 56.8 & 63.4 & 60.0 & 59.7 & 65.4 \\  & 1 & 15.7 & 10.4 & 21.8 & 23.5 & 32.5 & 25.9 & 24.2 & 23.7 & 27.7 \\  & 2 & 15.3 & 19.4 & 23.1 & 26.9 & 35.3 & 37.8 & 31.4 & 30.6 & 37.9 \\ \multirow{3}{*}{Set 2} & 3 & 22.7 & 29.6 & 27.6 & 34.1 & 39.1 & 46.6 & 39.5 & 38.4 & 38.0 \\  & 5 & 30.1 & 34.8 & 31.7 & 35.1 & 40.8 & 48.9 & 43.9 & 38.4 & 42.5 \\ \multirow{3}{*}{Set 3} & 10 & 40.5 & 45.4 & 43.0 & 39.1 & 43.8 & 51.1 & 49.0 & 48.5 & 48.6 \\  & 1 & 21.3 & 14.3 & 20.6 & 30.8 & 40.1 & 35.2 & 24.5 & 22.6 & 21.9 \\ \multirow{3}{*}{Set 3} & 2 & 25.6 & 18.2 & 23.9 & 34.8 & 41.5 & 42.9 & 36.1 & 33.4 & 44.5 \\  & 3 & 28.4 & 27.5 & 29.4 & 42.8 & 44.3 & 47.8 & 42.3 & 39.5 & 49.8 \\ \multirow{3}{*}{Set 3} & 5 & 42.8 & 41.2 & 43.9 & 49.5 & 46.9 & 54.8 & 49.2 & 47.3 & 55.5 \\  & 10 & 45.9 & 48.1 & 44.1 & 49.8 & 46.4 & 53.5 & 54.5 & 54.0 & 58.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of object detection under various few-shot conditions on the PASCAL-VOC dataset.