# Meta-YOLO: Meta-Learning for Few-Shot Traffic Sign Detection via Decoupling Dependencies
## 1 Introduction
We believe that this eliminates the impact of distribution shifts between training and testing data, which is vital for improving the detection accuracy and generalization ability of the constructed model. Therefore, we use the method of Random Fourier Features and sample weighting to effectively remove the statistical correlation between relevant and irrelevant features. Traffic sign detection is a key part of autonomous driving technology with good performance for both speed and accuracy. This requires a detector with the ability to detect unseen classes accurately in real time. Few-shot is a feasible approach to the above problem, which can detect new classes with only a small number of labeled samples needing to be trained. The current transfer learning is more effective for solving few-shot problems, but  
Figure 1: We aim to obtain a few-shot traffic sign detection model by training on the base classes with meta-learner guidance at image level; consequently, the model is able to detect novel objects on testing images with learning from a limited annotated sample. it needs to be trained on the source domain and then fine-tuned on the target domain with few samples, which is not suitable for dynamic environments and urgent tasks. Meta-learning provides a new and feasible solution to the above few-shot problem. We designed a traffic sign detector that unifies the few-shot learning ability by two-stage meta-learner \(\phi\) learning class features from base classes at the image-level and a multi-scale ability to predict novel classes in conformity with limited support examples. The proposed meta-learning FSD framework, shown in Figure 2 contains a meta-learner model F and a detector model \(\phi\). We extract query features and support features by utilizing the YOLOv4 backbone network, gaining the ability to quickly adjust parameters to be at the optimal level for new tasks with the meta-learner. Here, the learning process exists at two terms: the initialization term and adaptation term, shown in Figure 3, improving a strong discriminative ability of a detector to distinguish different categories from the multi-relation detector module. Our framework boils down exactly to a typical meta-learning paradigm, encouraging the name Meta-YOLO. **Our contributions are as follows**:  
1. We present Meta-YOLO, a novel few-shot traffic sign detection framework that unifies image-level meta-info of object localization and classification into a one-stage module. 2. We design a feature decorrelation module (FDM) that gets rid of spurious correlations and, in turn, focuses more on the real connection between discriminative features and labels. This module can overcome the complex backgrounds' interference in the detection process, thus enhancing the robustness of the system. 3. We introduce a three-head mechanism that allows the detector to jointly attend to information from the spatial position relationship of different levels. The application of this mechanism's advance ability allows a detector to distinguish between different categories as well as to detect different scale targets. The rest of this paper is organized as follows: in Section 2 we discuss details of the feature decorrelation module, while the architecture and implementation details of our network are presented in Section 3. We give experimental results in Section 4 and conclusions in Section 5. Figure 2: The architecture of our proposed Meta-YOLO. It comprises a meta-learner model and a detector model. The weights of the obtained query features and support features are shared by the feature extractor utilizing the YOLOv4 backbone network. The aggregator outputs the category detection results by combining the query features with the category features associated to the target instance in the support images received by the decorrelation module and the classifier. To perform feature multi-scale detection, a multi-relational detector module with a three-head mechanism is employed.