Zero-shot image classification based on attribute

Abstract

In the image classification task, traditional model can only recognize annotated image samples, but class labels can't involve all the object categories. In order to reduce the dependence on the labels and recognize unannotated object samples, this paper proposes zero-shot image classification based on attribute. The binary attribute is used as the intermediate knowledge to migrate learned knowledge from training samples domain to test samples domain. Using the classification model of the multi-loss function based on ResNet-50 to predict the object attributes. Then, using an attribute matrix to represent the correspondence between the object class and the attribute. Finally, the result of attribute prediction is combined with the prior knowledge of the attribute matrix to get the category. Compared with the traditional image classification method, the attribute learning model is applied to the zero-shot image classification. The experimental data show that the method improves the recognition accuracy of the image and improves the flexibility of the image classification task, which lays the foundation for the multi-source domain adaptation induction problem.