Machine Learning, Zero-Shot Learning, Computer Vision, Pattern Recognition.
Zero-shot learning (ZSL) aims to recognize unseen objects using disjoint seen objects via sharing attributes. The generalization performance of ZSL is governed by the attributes, which transfer semantic information from seen classes to unseen classes. To take full advantage of the knowledge transferred by attributes, in this paper, we introduce the notion of the complementary attributes (CAs), as a supplement to the original attributes, to enhance the semantic representation ability. Theoretical analyses demonstrate that CAs can improve the PAC-style generalization bound of the original ZSL model. Since the proposed CA focuses on enhancing the semantic representation, CA can be easily applied to any existing attribute-based ZSL methods, including the label-embedding strategy-based ZSL (LEZSL) and the probability-prediction strategy-based ZSL (PPZSL). In PPZSL, there is a strong assumption that all attributes are independent of each other, which is arguably unrealistic in practice. To solve this problem, a novel rank aggregation (RA) framework is proposed to circumvent the assumption. Extensive experiments on five ZSL benchmark datasets and the large-scale ImageNet dataset demonstrate that the proposed CA and RA can significantly and robustly improve the existing ZSL methods and achieve state-of-the-art performance.