An Interactive Class Attribute Construction Method For Zero-shot Image Classification

Zero-shot image classification can solve the image classification problem when the training data classes and test data classes are disjoint. Human annotation attributes are a commonly used auxiliary knowledge to achieve zero-shot image classification. Designing a suitable class attribute matrix requires comprehensive analysis by domain experts. It is a tedious and unguided process to define the similarities and differences of each category and define the attributes of each category. In order to assist expert users in designing a class attribute matrix for zero-shot image classification, this paper proposes an interactive construction method. First Through a concept-based deep learning interpretability method, attributes are extracted from the training set data; secondly, the importance of the extracted attributes is explored and analyzed through the interactive method of multi-view collaboration; thirdly, the system provides global and local ways to assist users in designing the attribute values of the test set data categories. Finally, a case analysis is carried out on the benchmark dataset of zero-shot learning. The experiments show that the system can help expert users to complete the construction of class attributes efficiently and conveniently.