# Meta-YOLO: Meta-Learning for Few-Shot Traffic Sign Detection via Decoupling Dependencies
## 3 Methodology
### Network Description  
We adopt the backbone of YOLOv4 (i.e., CSPDarknet53) to implement the feature extractor; support features share all learnable parameters with query feature following the philosophy [40]. We filter out irrelevant information within the support image via FDM whose further details have been shown in Section 2. Meta-YOLO is conceptually simple and aims to quickly and accurately detect unseen traffic signs. The framework is shown in Figure 2, which is mainly composed of two parts: meta-learner model \(\mathcal{F}\) and detection model \(\phi\). We set a learnable learning rate for all parameters so that the meta-learning system can learn good initialization as well as fast adaptation strategies. Given a query image, first the feature extractor generates its feature map and then adopts 1\(\times\)1 convolution to make the feature map's channel dimension compatible with the downstream modules. Unlike query feature, which retains image-level information, for support image it is necessary to extract the category features of the certain object instance. Therefore, we utilize Fourier features combined with sample weights to decouple dependencies and remove the irrelevant information present in the support set. The design of aggregator follows previous work [41], reweighting the query features \(\ell^{cls}\) according to the output of the support set class feature \(\ell^{\textit{tryy}}\). The support class features are combined with query features as in Equation (8) to obtain the detection results of the corresponding category. \[\mathcal{A}\left(\ell^{\textit{tryy}},\ell^{cls}\right)=\ell^{\textit{tryy}} \otimes\ell^{\textit{cls}} \tag{8}\]  
where \(\otimes\) represents channel-wise multiplication. The support set is utilized in a loop to construct a classifier for the sample class. The aggregator is then used to cascade the information matching to the query set with the feature relationships and categories corresponding to each image in the support set. The detector we desire is with a powerful discriminative capacity to distinguish between different categories as well as to detect different scale targets. Based on this, three-heads, the global-relation head, the local-correlation head and patch-relation head, are constructed to learn a deep embedding for global matching, pixel-wise and depth-wise cross-correlation between support and query sets and a deep non-linear metric for patch matching, respectively. **Revisiting Yolov4 backbone. ** The proposed Meta-YOLO utilizes the successful YOLOv4 for FSD to detect traffic signs, shown in Figure 2. YOLO [5; 6; 7; 8] is the representative work of one-stage model; the core idea is to input the complete image into the network and regress the classes and coordinates of the bounding box in the output layer to achieve end-to-end training. YOLOv4 [8] improves on YOLO v3 [7] to sit at the faster end of the speed-accuracy trade-off. Its accuracy is already comparable to or even surpasses two-stage target detection algorithms while maintaining a very high speed. The YOLOv4 backbone uses CSPDarknet53 with the mish activation function to increase the network's learning ability and gradient transmission efficiency; the network's regularization process is upgraded and improved extensively by employing a better Dropblock. CSPDarknet53 is a backbone structure based on the Yolov3 backbone network Darknet53, which draws on the experience of CSPNet 2019. The use of this network structure enhances the learning capability of CNNs, enabling a light weight while sustaining accuracy, decreasing computational bottlenecksand lowering memory costs. This is of great significance to YOLO, which not only ensures the speed and accuracy of inference but also reduces the size of the model. This work aims to address the problem of few-shot traffic sign detection under the blessing of meta-learning. Then we evaluate the feasibility of the framework by equipping it with an object detection architecture; to this end, relatively simple and efficient detectors should be selected, i.e., YOLOv4.