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Robust object recognition systems usually rely on powerful feature extractionmechanisms from a large number of real images. However, in many realisticapplications, collecting sufficient images for ever-growing new classes isunattainable. In this paper, we propose a new Zero-shot learning (ZSL)framework that can synthesise visual features for unseen classes withoutacquiring real images. Using the proposed Unseen Visual Data Synthesis (UVDS)algorithm, semantic attributes are effectively utilised as an intermediate clueto synthesise unseen visual features at the training stage. Hereafter, ZSLrecognition is converted into the conventional supervised problem, i.e. thesynthesised visual features can be straightforwardly fed to typical classifierssuch as SVM. On four benchmark datasets, we demonstrate the benefit of usingsynthesised unseen data. Extensive experimental results suggest that ourproposed approach significantly improve the state-of-the-art results.

From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis