Zero-shot Learning with Complementary Attributes

Zero-shot learning (ZSL) aims to recognize unseen objects using disjoint seen objects via attributes to transfer semantic information from training data to testing data. The generalization performance of ZSL is governed by the attributes, which represent the relatedness between the seen classes and the unseen classes. In this paper, we propose a novel ZSL method using complementary attributes as a supplement to the original attributes. We first expand attributes with their complementary form, and then pre-train classifiers for both original attributes and complementary attributes using training data. After ranking classes for each attribute, we use rank aggregation framework to calculate the optimized rank among testing classes of which the highest order is assigned as the label of testing sample. We empirically demonstrate that complementary attributes have an effective improvement for ZSL models. Experimental results show that our approach outperforms state-of-the-art methods on standard ZSL datasets.