# Study on Few-Shot Object Detection Approach Based on Improved RPN and Feature Aggregation
## 2 Related Work
### Few-Shot Object Detection  
FSOD not only requires training the model to recognize new classes, but also requires the localization of objects in the image. There have been several early attempts in using meta-learning for FSOD. For example, [PERSON] et al. [5] and [PERSON] et al. [7] used meta learners to apply feature re-weighting schemes to single-stage object detectors (YOLOV2 [5]) and two-stage object detectors (Faster R-CNN [8]). These meta-learners will support images (e.g., a small number of new/base class labeled images) and bounding box annotations as inputs. In [5], a weight prediction meta model was proposed to predict the parameters of class-specific components from a few samples while learning class-unknown components from base class samples. In [13], the authors enhance the generalization ability of the model on new classes from the perspective of sample diffusion. In [14], the authors optimize feature space partitioning and novel class representation to achieve FSOD. Although these methods have improved the performance of object detection to some extent, some scholars have found that they mainly focus on the feature interaction of the detection head and ignore feature extraction. [PERSON] et al. [15] conducted feature interaction simultaneously in the backbone network and detection head. [PERSON] et al. aggregated image/ROI level query features with support features generated by meta-learners [7]. There are also other similar designs, such as feature aggregation schemes ([PERSON] and [PERSON] et al. [16], [PERSON] et al. [17], [PERSON] et al. [9], [PERSON] et al. [18], [PERSON] et al. [19]) and feature space enhancement ([PERSON] et al. [20], [PERSON] et al. [21], [PERSON] et al. [22]) were explored. Unlike meta-learning, [PERSON] et al. proposed a simple two-stage fine-tuning approach (TFA) that significantly improves FSOD performance by fine-tuning only the last layer [6]. There are also a series of works following TFA ([PERSON] et al. [23], [PERSON] et al. [24], [PERSON] et al. [25]). Label-Verify-Correct uses pseudo labeling methods to increase the number of instances to reduce class imbalance [26]. However, most of these methods use RPNs trained in base classes to generate new candidate regions. Therefore, for a limited number of new classes, the introduction of new class images creates complex backgrounds that are different from the target object, and new class boxes may be considered as background regions in the base class, resulting in the loss of high threshold candidate boxes. In addition, these methods mainly focus on inter-class support and query feature aggregation, with less consideration given to feature information issues caused by scale in the RoI stage. We have adopted a meta-learning architecture and proposed an improved RPN and feature aggregation module to effectively alleviate the aforementioned issues. In general, the existing FSOD methods have the following shortcomings: (1) Lack of accuracy. Although the FSOD methods have made some progress, the overall performance of most FSOD methods still cannot reach the required accuracy. This limits the universality and reliability of FSOD in practical applications. (2) The ability to identify new categories is limited. Due to the scarcity of training samples, the FSOD model often performs poorly in identifying new categories. This reduces the generalization ability of the model and makes it difficult to adapt to the changing environment and requirements. (3) The FSOD model is usually trained on the basis of a large number of labeled samples, and then fine-tuned on the novel classes with a small number of labeled samples. This training method is easy to lead to the bias of the model towards the basic class, thus ignoring or misjudging the novel class. (4) RPN plays a key role in FSOD, but it is difficult for RPN to accurately identify the target area of novel class because it may be affected by basic class data in the training process.