An improved indirect attributeweighted prediction model for zero-shot image classification

Abstract

Zero-shot learning refers to the object classification problem where no training samples are available for testing classes. For zeroshot learning, attribute transfer plays an important role in recognizing testing classes. One popular method is the indirect attribute prediction (IAP) model, which assumes that all attributes are independent and equally important for learning the zero-shot image classifier. However, a more practical assumption is that different attributes contribute unequally to the classifier learning. We therefore propose assigning different weights for the attributes based on the relevance probabilities between the attributes and the classes. We incorporate such weighed attributes to IAP and propose a relevance probability-based indirect attribute weighted prediction (RP-IAWP) model. Experiments on four popular attributed-based learning datasets show that, when compared with IAP and RFUA, the proposed RP-IAWP yields more accurate attribute prediction and zero-shot image classification.