## 1 Introduction
ST-FSOD [23] employs self-training to generate pseudo-samples in order to address the issue of confusion between background and foreground features. However, it relies on a limited number of novel class samples for supervision, which can easily lead to overfitting. Therefore, it is essential to develop a method that can effectively distinguish the class-specific features of each foreground instance while differentiating them from the background features. Motivated by the aforementioned methods and in order to address these issues, we introduce MSOCL [22] as the baseline network and propose a new two-stage FSOD framework for remote sensing images based on transfer learning via pseudo-sample generation and feature enhancement called PSGFE. Specifically, we employ a pseudo-sample generation module (PSGM) to identify potential foreground instances within the background and incorporate them into training, effectively preventing confusion between foreground features and background features. Additionally, we introduce a feature enhancement module (FEM) to mitigate class confusion caused by multi-scale variations, high inter-class similarity, and significant intra-class variability in remote sensing images. FEM combines depthwise separable convolutions [24; 25] with large-kernel convolutional networks [26] to enhance the features of regions of interest (ROI). This method employs large-kernel convolutions to enlarge the receptive field and extract global features from the image, while depthwise separable convolutions are utilized to capture local features. Concurrently, convolutional layers with multiple kernel sizes dynamically capture multi-scale features of instances. This approach enables the model to focus on the most critical regions within the ROI and to learn salient classification features from complex and diverse foregrounds. PSGM is proposed to more effectively excavate potential objects within the background, which consists of two modules: the self-training-based pseudo-proposal generation network (STPPGN) and the self-training-based confident pseudo-sample generation network (STCPSGN). STPPGN generates an initial set of pseudo-proposals in the unlabeled background regions. These pseudo-proposals are then filtered and refined by STCPSGN to produce high-quality pseudo-samples, which are subsequently integrated into the training set. STPPGN focuses on the background regions and extracts high-confidence regions to supervise the generation of pseudo-proposals, thereby avoiding the issue in ST-FSOD [23] where the student region proposal network (RPN) relies solely on a limited number of novel class samples for supervision, leading to overfitting on the scarce data and consequently reducing its generalization ability. Additionally, STCPSGN separates the bounding box-head (BBH) for generating pseudo-samples from that for processing samples, thereby avoiding interference. The overall architecture of PSGFE is depicted in Figure 1a, where Branch 1 is the Faster-RCNN [27] object detection branch augmented with an FEM. Branch 2 is the pseudo-sample generation branch, which is our PSGM, consisting of STPPGN and STCPSGN. Branch 3, inspired by the contrastive learning approach [22], facilitates the model in learning multi-scale features of instances by cropping labeled instance objects and conducting contrastive learning with the proposals or pseudo-samples generated by the other two branches. Our main contributions are as follows:  
(1) We propose a pseudo-sample generation module called PSGM to generate pseudo-samples from unlabeled background data. This enables our method to learn the true background features, thereby distinguishing between the real background and potential novel class instances, reducing confusion between them, and enhancing the performance of PSGFE. (2) We propose an attention mechanism-based feature enhancement module that augments the features of both the pseudo-proposals and ground truth instances within the ROI.