Joint Projection and Subspace Learning for Zero-Shot Recognition

Abstract

Zero-shot learning (ZSL) typically learns a projection function between a feature space and a semantic space (e.g. attribute space) to recognize unseen classes without any training samples. However, when the projection function learned only with seen class training samples is applied to unseen class test samples, most existing ZSL models suffer from projection domain shift. In this paper, we propose a new ZSL model, termed joint inductive learning (JIL) of bidirectional projections and shared subspace, to learn more generalizable projections and thus algin the seen and unseen class domains. By inducing the two tasks of bidirectional projection learning (BPL) and shared subspace learning (SSL), we integrate three forms of projection learning into a unified framework, which makes our JIL model become more generalizable across the two domains. Moreover, since a shared subspace is actually learned between the feature and semantic spaces, our JIL model can be naturally applied to cross-modal retrieval (CMR). Extensive experiments demonstrate that our JIL model generally outperforms the state-of-the-art alternatives in the two challenging tasks (i.e. ZSL and CMR).