## 4 Experiments
### Qualitative Results  
We visualize the novel classes detection results of our method and the baseline, in the DIOR dataset on Split1 20-shot and Split2 20-shot settings, as depicted in Figures 4 and 5. Compared to the baseline, our method exhibits superior detection performance. In featuring a multitude of dense small object instances, as observed in columns 1-2 of Figures 4 and 5, the baseline shows a higher rate of missed objects and is outperformed by PSGFE. In column 1 of Figure 4, the baseline fails to detect isolated ships, some airplanes, and the small bridge over the river. In Figure 5, it fails to detect airplanes, vehicles, and storage tanks. In contrast, our method is capable of detecting these instances, resulting in a lower rate of missed instances. The reason is that during the few-shot fine-tuning phase, there are only a few instances per class, and most instances in such dense images are not provided with annotations, leading the baseline to treat these potential instances as negative samples during contrastive learning, thereby confusing the features of the foreground and background. Our PSGM recalls these instances and generates pseudo-samples refining predictions and excluding these pseudo-samples from negative pairs in contrastive learning, thereby enhancing the detection rate.  
In the detection of object instances with high similarity but different scales, as shown in column 3 in Figure 5, both PSGFE and the baseline can detect tennis courts and basketball courts. However, PSGFE yields detection results that are closer to the magenta ground truth (GT) bounding box than those of the baseline. This indicates that our proposed FEM can better distinguish the features of similar objects across different classes and extract effective features from instances of the same class at different scales, thus improving the accuracy of the bounding box.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method/Shot** & **1** & **2** & **3** & **5** & **10** \\ \hline Meta-RCNN [28] & 2.7 & 4.7 & 11.4 & 32.7 & 42.7 \\ TFA [31] & 12.0 & 18.8 & 20.9 & 22.7 & 35.6 \\ FSCE [21] & 19.5 & 25.6 & 28.0 & 28.4 & 43.7 \\ CCAFR [51] & 20.5 & 24.0 & 30.2 & 32.6 & 45.8 \\ SAAN [38] & 4.7 & 9.0 & 10.5 & 34.1 & 44.0 \\ MSOCL [22] & 21.4 & 25.1 & 28.1 & 35.9 & 47.3 \\ \hline Ours & **22.6** & **26.3** & **31.6** & **37.5** & **50.7** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mean average precision (mAP) at different methods on the RSOD dataset is presented in percentage terms. The reported accuracies of all models are the percentage accuracies of mAP50 for the novel class obtained through five random sampling training sessions. The bold parts in the tables indicate the best results under each setting.  
Figure 3: (**a**) Comparison of mAP50 of novel class between PSGFE and MSOCL [22]. CCAFR [51] in RSOD datasets. (**b**) Variation in PSGFE relative to MSOCL [22] and CCAFR [51] in RSOD datasets.  
However, our method still has some limitations. As shown in the first column of Figure 5, some airplanes are missed in our detection results. In Split2, the airplane is a base class. The possible reason for the missed detections is that our method experienced catastrophic forgetting [52] of the features of the base classes while learning the features of novel classes during fine-tuning, which led to a decrease in the detection performance of the base classes and consequently caused the misses. In future research, we will explore effective mechanisms to retain base class knowledge and reduce forgetting of base classes.  
Figure 4: Visualizations of detection results for novel classes in DIOR datasets on Split1 under the 20-shot settings. In both columns, the first row displays the ground truth labels, the second row presents the baseline results, and the third row showcases the results of PSGFE.