Attribute subspaces for zero-shot learning

Abstract

Zero-shot learning (ZSL) aims to recognize unseen categories without corresponding training samples, which is a practical yet challenging task in computer vision and pattern recognition community. Current state-of-the-art locality-based ZSL methods aim to learn the explicit locality of discriminative attributes, which may suffer from insufficient class-level attribute supervision. In this paper, we introduce an Attribute Subspace learning method for ZSL (AS-ZSL) to learn implicit attribute composition, which is more general than attribute localization with only class-level attribute supervision. AS-ZSL exploits subspace representations that can effectively capture the intrinsic composition of high-dimensional image features and the diversity within attribute appearance. Furthermore, we develop a subspace distance based triplet loss to improve the distinguishability of the attribute subspace representation. Attribute subspace learning module is only needed for the training phase to jointly learn discriminative global features. This leads to a compact inference phase. Furthermore, the proposed AS-ZSL can be naturally extended to adapt to the transductive ZSL setting using a novel self-supervised training strategy. Extensive experimental results on several widely used ZSL datasets, i.e., CUB, AwA2, and SUN, demonstrate the advantage of AS-ZSL compared with the state-of-the-art under different ZSL settings.