# Wheel Hub Defects Image Recognition Base on Zero-Shot Learning
## 2 Related Work
### Visual-Semantic Association Mapping Learning  
Semantic embedding space is a bridge between image feature space and tag semantic space, and the establishment of vision-semantic association is an essential cornerstone of zero-shot learning, as shown in Figure 2.  
In general, once the vision-semantic association is established, we can calculate the similarity between any unseen class and the unseen class prototype and classify the sample \(x\) based on the similarity. Among them, the quality of the vision-semantic relationship directly affects the performance of zero-shot learning. On the one hand, the vision-semantic relationship learned from the training data should have sufficient generalization ability, so that it can be applied to the unseen mutually exclusive data to the greatest extent. On the other hand, it should also have a strong judgment ability, so as to have a positive impact on the subsequent recognition process based on similarity. We can model the vision-semantic relationship in the following three ways (as shown in Figure 2). The first one is to map image features to the semantic embedding space (we call it forward mapping, as shown in Figure 2a), and to identify unseen classes within the semantic embedding space. In the second way, image features and class semantics are embedded and simultaneously mapped to a common space (known as a common mapping, as shown in Figure 2b), and modeled in a common space. The third one is to map the semantic embedding vector to the image feature space (called reverse mapping, as shown in Figure 2c) and identify unseen classes in the image feature space. Among them, forward mapping has been a mainstream mapping method since the zero-shot learning problem was first applied in computer vision in 2009. In addition, common mapping first appeared in the form of a bilinear equation in 2013, and it has been widely adopted by researchers due to its good judgment and generalization ability. However, reverse mapping appeared in 2015 and has been on the rise. One of the reasons is that the image features extracted with deep learning technology have a good structure, and the same type of data are usually distributed in clusters, with stronger judgment ability. In contrast, the application time of reverse mapping in zero-shot learning is relatively short, and there is still a certain development space in the future. Both forward mapping and common space mapping methods employ the embedding function of samples and semantic descriptors, which learn embedding by minimizing the similarity function between the sample and the corresponding semantic descriptor, with differences only in the embedding method and the selection of the similarity function. These methods, after embedding, are typically categorized using a nearest neighbor search. However, in the high-dimensional space, the nearest neighbor search will suffer from the \"hubness\" problem (a certain number of data points will become the nearest neighbor or center of almost all test points), resulting in misclassification [36]. However, if the inverse mapping strategy [37] is adopted, namely mapping from the semantic space to the visual space, the hubness problem can be effectively avoided. Inspired by this article, we adopt the inverse mapping strategy in this paper. We further introduce the concept of relative features using pair relationships between data points. This not only provides additional structural information about the data but also reduces the dimension of the feature space, thereby reducing the hubness problem. However, the reverse mapping strategy is learned from the data of seen classes, so it is inevitable to suffer from the problem of projection domain shift. [PERSON] et al. [38] used multiple semantic information sources to explore the projection domain shift and implemented label transfer for unlabeled data of unseen classes to solve this problem. Recent research results,  
Figure 2: Visual semantic mapping methods.