Zero-Shot Classification Based on Multitask Mixed Attribute Relations and Attribute-Specific Features

Zero-shot classification is a hot topic in computer vision and pattern recognition. Most zero-shot classification methods are based on the intermediate level representation of attributes to achieve knowledge transfer from the training classes to the unseen test classes. Recently, multitask learning (MTL) has been shown as one of state-of-the-art approaches for attribute learning and zero-shot classification. Aiming at the attribute relation learning, features shared by attributes learning and attribute heterogeneity, we propose a zero-shot classification based on multitask mixed attribute relations and attribute-specific features. First, considering the relationship between attribute-attribute and attribute-features, a second-order attribute relation and attribute-specific features learning model is constructed from training samples based on MTL. Second, second-order attribute relation is extended to high-order attribute relation and multiple attribute classifiers are learned. Finally, zero-shot classification is completed based on the maximum posterior probability. Experimental results on AWA and PubFig data sets show that the proposed method can yield more accurate attribute prediction and zero-shot classification compared with several multitask attribute learning methods.