From Zero-shot Learning to Conventional Supervised Classification: Unseen
Visual Data Synthesis
Abstract
Robust object recognition systems usually rely on powerful feature extraction mechanisms from a large number
of real images. However, in many realistic applications,
collecting sufficient images for ever-growing new classes is
unattainable. In this paper, we propose a new Zero-shot
learning (ZSL) framework that can synthesise visual features for unseen classes without acquiring real images. Using the proposed Unseen Visual Data Synthesis (UVDS) algorithm, semantic attributes are effectively utilised as an
intermediate clue to synthesise unseen visual features at
the training stage. Hereafter, ZSL recognition is converted
into the conventional supervised problem, i.e. the synthesised visual features can be straightforwardly fed to typical
classifiers such as SVM. On four benchmark datasets, we
demonstrate the benefit of using synthesised unseen data.
Extensive experimental results suggest that our proposed
approach significantly improve the state-of-the-art results