# Meta-YOLO: Meta-Learning for Few-Shot Traffic Sign Detection via Decoupling Dependencies
## 1 Introduction
Based on this consideration, we combined the meta-paradigm with CNN to promote the robustness to unseen classes' tasks. **Few-shot object detection. ** Since the performance of large neural networks is limited by the size of the training set, a small number of samples within the training set can easily lead to overfitting of the network and failure to realize the potential of deep networks. Few-shot learning is a method of deep learning training and prediction with insufficient sample data. In few-shot tasks, models trained with small samples can easily fall into overfitting to small samples as well as underfitting to the target task. Meta-learning has shown great potential in solving few-shot problems [19; 20; 21; 22], and its unique implementation can improve the accuracy of target detection for the classification of novel categories. The main idea is to use meta-info accumulated from historical tasks as prior knowledge, and then learn a small number of target samples to quickly master new tasks. This method has strong adaptability and robustness to unseen scenarios. Meta-learning approaches used for few-shot detection are roughly divided into three groups: gradient-based [23; 24], nearest-neighbor [25; 26] and model-based [27; 28]. [PERSON] et al. [29] proposed a multi-scale few-shot detection model based on fine-tuning, which utilizes residual involution blocks to construct the all feature learning architecture as well as design PAM to aggregate from all feature levels. This method exploits shallow feature semantic information for object location in the first stage and is partly fine-tuned on a small balanced dataset in the second stage. However, [PERSON] et al. [30] visualized the feature distribution of samples in the pre-training space, proving that fine-tuning has limited performance improvement in meta-learning and can easily increase the risk of base task overfitting. Therefore, in this work, we take meta-info to update the meta-learner to replace fine-tuning. [PERSON] et al. [31] proposed a general object detection system, which combines the feature-based domain attention mechanism with sequence and exception networks, and assignsnetwork activation to different domains through SE adapter library learning, so as to automatically obtain the importance of each feature channel. The core idea of SENet is to learn the feature weight according to the loss, so that the weight of an effective feature map is large, and the weight of an ineffective feature map is small, so as to achieve better results. However, the general detection system ignores the problem of spatial dislocation, which leads to the poor performance in detecting traffic signs with small targets and a chaotic background. [PERSON] et al. [32] improved the problem of training on base training to generate candidate proposals for novel classes and missing high IOU boxes in the RPN stage. A coarse-grained prototype matching network (meta-RPN) was proposed, which takes a non-linear classifier based on metric learning to replace the traditional linear target classifier, dealing with the similarity between anchor boxes and novel classes in query images, so as to improve the recall of the few novel class candidate boxes. A fine-grained prototype matching network (meta-classifier) was designed. The network has spatial feature alignment and foreground attention modules to deal with the similarity between noise and novel classes, so as to enhance the overall detection accuracy. However, within the meta optimizer lies the problem of prototype deviation. The reason for this problem is the use of an average-based method to roughly estimate the gradient when the labeled samples are limited in each category. Given the above considerations, in this work, our aim is to address the challenging few-shot traffic sign detection problem, as shown in Figure 1. Specifically, given the problem of unbalanced sample distribution, we aim to obtain a model that can detect both base and novel objects at test time.