# Study on Few-Shot Object Detection Approach Based on Improved RPN and Feature Aggregation
## 1 Introduction  
In recent years, with the continuous development of deep neural networks, object detection technology has made tremendous progress, and a series of high-performance detectors have emerged. However, traditional object detection methods require a large amount of annotated images and considerable time, labor, and cost. Under low data volume conditions, due to the diversity of data, it is often difficult to achieve good performance with a small amount of data [1]. How to make the model learn useful knowledge with limited labeled samples has become a new challenging task [2]. In the context of few-shot recognition, people are committed to solving the problem of few-shot object detection (FSOD), so FSOD has gradually become a research hotspot [3]. Early attempts introduced the idea of few-shot classification methods in FSOD, which was adjusted accordingly. For example, using the meta-learning paradigm [4], the meta detector is first trained in the base class to learn the prior knowledge of the base class and then updated in the new class to make predictions. [PERSON] et al. first applied meta-learning to detection and proposed to add a meta feature learner and weight module to YOLOv2 to solve the problem of few-shot detection [5]. Another corresponding approach is the fine-tuning-based FSOD method, which typically includes two steps: (1) pretraining the model on a rich base class. (2) Fine-tuning the model to adapt to the new class. [PERSON] et al. proposed a simple methodof freezing the feature extractor while only fine-tuning the classification and regression branches of the detector, which achieved competitive results [6]. [PERSON] et al. proposed treating each supporting example as a single point in the feature space and averaging all features as class prototypes [7]. Despite some progress being achieved, existing methods are far less effective on new classes than on base classes. The previous methods treated supporting and querying data as a single task, only considering the aggregation of support and query features between similar classes, without establishing associations between heterogeneous classes, and ignoring more feature information [5; 6; 7]. In addition, previous methods did not improve RPN. The original RPN mainly distinguished foreground and background, and used RPN trained on base class data to generate candidate boxes for new classes [8]. However, for a limited number of new classes, the introduction of new class images produces complex backgrounds that are different from the object. RPN may misclassify images with large background areas, resulting in the omission of some high IoU value candidate boxes. To address the above issues, we propose a few-shot object detection method based on an improved PRN and feature aggregation using a two-stage approach based on Faster R-CNN [8], which is based on [PERSON]'s foundation [6]. This method mainly consists of a feature extractor, an improved RPN, RoI Align, and an introduced feature aggregation module [9]. In our works, a nonlinear classifier has been added to the improved RPN [10], which establishes a similarity matrix by calculating the similarity between the features extracted by the backbone network and the new class features [11], obtaining high IoU candidate regions and improving the performance of object detection. At the same time, a feature aggregation module is introduced in RoI Align to enhance feature aggregation through attention mechanisms, aggregating features generated from RoIs of different scales to alleviate the problem of information loss caused by scale differences. Finally, normalization is performed in softmax to balance each feature information and obtain the final object detection result. The entire network is called IFA-FSOD, and our model has achieved competitive results compared to advanced methods (SOTAs, state-of-the-arts). By fine-tuning on the new class, we can achieve SOTA precision on multiple FSOD benchmarks, further demonstrating the performance improvement of the model in FSOD.