# Embedded Zero-Shot Image Classification Based on Bidirectional Feature Mapping
Therefore, in order to enable the trained model to reason on the basis of the knowledge learned on the seen class for the purpose of classifying the samples of the unseen class, it is necessary to add the descriptive information for each class in the zero-shot image classification, which is called auxiliary information here to communicate the seen class and the unseen class, as shown in Figure 2.  
Zero-shot image classification accomplishes knowledge transfer from seen to unseen classes through partial semantic information shared between the seen and unseen classes. Auxiliary information is usually characterized by relevant common-sense information or prior knowledge and is used to enhance the model's ability to learn shared partial semantic information. In early zero-shot studies, manually defined attributes (e.g., \"wing color\", \"beak shape\", etc.) were often used as auxiliary information. With the development of technology, word embeddings [10] of category labels, text embeddings [11] of linguistic descriptions of images, knowledge graphs [12], etc., can be used as auxiliary information. All of these types of auxiliary information can characterize the semantic information of the categories and the similarity between different categories; so they can be used as auxiliary information to help the model realize the knowledge migration from the seen to the unseen classes to complete the zero-shot image classification task. In zero-shot image classification, the quality of auxiliary information is closely related to the effectiveness of knowledge migration and the final model classification result. Figure 1: Difference between traditional image classification and zero-shot image classification. After the zero-shot image classification technique was proposed, a wide variety of methods have emerged. However, it has been found that most of the current methods can be categorized into two paradigms, one is the embedding-based zero-shot image classification method and the other is the generative-based zero-shot image classification method. Embedding-based methods map image features and semantic information into some space and subsequently classify them according to strategies such as nearest neighbor search. In generalized zero-shot image classification, embedded models usually suffer from the problem of bias towards seen classes. Moreover, previous embedded models usually adopt a single feature mapping approach, such as visual-semantic mapping or semantic-visual mapping, and this single mapping approach cannot fully explore the information embedded in the features and the connection between two features. Although generative-based zero-shot image classification methods can solve the seen class bias problem, they require a large amount of computational resources for training. Based on the above observations, we propose a feature space mapping module dominated by a bidirectional feature mapping network (BFM), which consists of an attribute-to-visual mapping network and a visual-to-attribute mapping network, which captures a more comprehensive relationship between visual and semantic features to make full use of the information contained in both visual and semantic features. A visual-to-category labeled semantic feature space mapping network is used as a supplement to enable the model to learn richer semantic information. Attention mechanisms based on attribute guidance and visual guidance are then used to weight the features to reduce the discrepancy between visual and semantic features to mitigate the modal difference problem, and then the category calibration loss is used to assign a larger weight to unseen classes and a smaller weight to seen classes to mitigate the seen class bias problem. In addition to this, we introduced the self-attention mechanism to dynamically focus on the sample visual features and enhance the representation of visual features. The experimental results verify the superiority of BFM.