Interactive Class Attribute Construction Method for Zero-Shot Image Classification

Zero-shot image classification addresses the problem of non-overlapping categories between training and testing data. Human-annotated attributes are a commonly used form of auxiliary knowledge in zero-shot image classification. To assist experts in designing class attribute matrices, an interactive construction method is proposed, simplifying the tedious and unguided process. First, understandable attribute information is extracted from the training dataset images using a concept-based deep learning explainability method. Then, an interactive approach involving multi-view collaboration is employed to explore and analyze the importance of the extracted attributes. The system offers both global and local options to aid users in designing attribute values for testing dataset categories. Finally, the effectiveness and practicality of the design method are validated through case studies on the Animals with Attributes2 dataset and user evaluation experiments using the Likert scale, facilitating efficient and convenient class attribute construction for expert users.