## 2 Related Work  
### Few-Shot Object Detection  
Currently, there are two main approaches to FSOD: meta-learning-based methods and transfer-learning-based methods.  
Meta-learning methods focus on transferring prior knowledge from well-annotated base classes to sparsely annotated novel classes by simulating a series of few-shot tasks. These methods mainly consist of two branches: a query branch and a support branch. The query branch learns object detection tasks from query images containing both base and novel classes, while the support branch provides auxiliary information and feature  
Figure 1: (**a**) The overall framework of PSGFE. (**b**) The internal structure of STPPGN, composed of a set of teacher-student RPNs, used to generate pseudo-proposals. (**c**) The internal structure of STCPSGN, composed of a set of teacher-student box-heads, used to further screen and process the pseudo-proposals, ultimately generating pseudo-samples. (**d**) The internal structure of FEM, where Conv0 through Conv5 each denote a distinct convolutional group, with each group containing multiple convolutional operations that include a large kernel convolution and DWConv is depthwise separable convolution.  
representations for both base and novel classes, enabling the query branch to better adapt to different object classes. A common meta-learning approach involves extracting specific and representative class prototypes from query images, followed by performing specific aggregation operations, such as weighted summation, concatenation, or attention mechanisms with support vectors to improve the ability of the model to recognize novel class objects [28; 29; 30].  
Compared to meta-learning approaches for FSOD, transfer learning methods employ relatively simpler learning strategies [10]. Fine-tuning is a commonly used transfer learning technique, involving pre-training a base-class model on a voluminous annotated dataset, followed by fine-tuning on a small number of target-domain samples. The two-stage fine-tuning approach (TFA), which fine-tunes only the classification and regression branches of the detector while keeping the feature extractor frozen, is one of the most commonly used transfer learning methods [31]. According to TFA, numerous methods have been proposed to address specific challenges in FSOD. Examples include FSCE [21], UP-FSOD [32], and DeFRCN [33]. However, TFA comes with its own set of limitations: (1) Merely fine-tuning can result in the erosion of source-domain knowledge, which in turn diminishes the transferability of the model. (2) Object detectors find it challenging to differentiate between class-agnostic and class-specific knowledge without explicit modeling.  
Generative adversarial networks (GANs) [34] have been explored for data augmentation in FSOD by synthesizing realistic samples through adversarial training. CLIP-GAN [35] generates synthetic instances from text prompts, which are then pasted into real backgrounds to enrich training data and improve novel class detection. SS-GAN [36] uses self-supervised GANs for unsupervised few-shot recognition, with the aim of improving generalization through adversarial feature learning. Although these methods effectively enhance model performance by generating new instances, they fail to address the confusion between background and foreground features, which arises from the inability to mine unlabeled foreground instances from the background. To resolve this issue, a method is needed to effectively mine potential foreground instances from the background.