## 2 Related Work
### Few-Shot Object Detection in Remote Sensing Images  
In remote sensing, existing FSOD methods are divided into two classes: meta-learning and transfer learning.  
Meta-learning methods, such as FSPDM [37] and SAAN [38], acquire knowledge that transcends individual tasks through meta-learning processes. Specifically, P-CNN [39] mitigates the sparse directional space in novel classes due to sample scarcity by employing a prototype-guided RPN. These methods rely on both query and support sets to discern inter-class relationships, a complex procedure. Thus, a simpler detector is still needed.  
Transfer learning methods simplify data preparation workflows and procedures. Inspired by FSDet [40]. various two-stage fine-tuning transfer learning models focusing on multiscale feature aggregation have emerged for remote sensing images [41; 42]. Inspired by the contrastive learning approach employed in FSCE [21] for clustering proposal features from RPN, AACE [43] enhances feature distinction by integrating a scale-aware search module and a contrastive ROI branch. MSCCL [22] constructs positive and negative sample pairs using proposal features and cropped instance object features, and then conducts contrastive learning. This process brings the features of positive pairs closer in the feature space while pushing those of negative pairs further apart, thereby enhancing the discriminative power of the features. However, negative sample pairs may include unannotated objects, which can interfere with feature learning and degrade performance. Pseudo-sample generation addresses this by mining potential foreground objects in the background to generate pseudo-samples and removing negative samples that highly overlap with these pseudo-samples, thus avoiding interference.  
Self-training uses model predictions for unlabeled data to produce pseudo-samples, which are then integrated into the training process to boost the performance of the model on targeted tasks [44]. ST-FSOD [23] utilizes a self-training-based RPN (ST-RPN) to generate proposals for novel classes, utilizing only a limited number of novel class samples for supervision while ignoring areas in the background that are likely to contain novel class objects. In BBH, temporary labels are generated and assigned to ROIs labeled as background. This method reduces confusion between the background and the novel class foreground, thereby enhancing the detection performance.  
However, supervising the ST-RPN with only a few novel class samples may lead to overfitting, and the ignored regions it generates could also contain labels. This situation may further exacerbate the confusion between the foreground features and the background features, leading to a decrease in performance. Furthermore, the shared BBH for pseudo-samples and samples may hinder the extraction of salient features. Motivated by this insight, we propose PSGM which effectively solves this issue. STPPGN focuses solely on background regions without any annotations and extracts pseudo-proposals. STCPSGN classifies and regresses pseudo-proposals to form pseudo-samples, enriching the dataset, and concurrently separates the BBH for generating pseudo-samples from that for processing samples to avoid interference.