Zero-shot recognition with latent visual attributes learning

Zero-shot learning (ZSL) aims to recognize novel object categories by means of transferring knowledge extracted from the seen categories (source domain) to the unseen categories (target domain). Recently, most ZSL methods concentrate on learning a visual-semantic alignment to bridge image features and their semantic representations by relying solely on the human-designed attributes. However, few works study whether the human-designed attributes are discriminative enough for recognition task. To address this problem, we propose a couple semantic dictionaries (CSD) learning approach to exploit the latent visual attributes and align the visual-semantic spaces at the same time. Specifically, the learned visual attributes are elegantly incorporated into the semantic representation of image feature and then consolidate the discriminative visual cues for object recognition. In addition, existing ZSL methods suffer from the domain shift issue due to the source domain and target domain have completely separated label spaces. We further employ the visual-semantic alignment and latent visual attributes jointly from source domain to regularise the learning of target domain, which ensures the expansibility of information transfer across domains. We formulate this as an optimization problem on a unified objective and propose an iterative solver. Extensive experiments on two challenging benchmark datasets demonstrate that our proposed approach outperforms several state-of-the-art ZSL methods.