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Type: Conference paper
Title: Generalised zero-shot learning with domain classification in a joint semantic and visual space
Author: Felix Alves, R.
Harwood, B.
Sasdelli, M.
Carneiro, G.
Citation: Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA 2019), 2019, pp.1-8
Publisher: IEEE
Publisher Place: online
Issue Date: 2019
ISBN: 9781728138572
Conference Name: International Conference on Digital Image Computing: Technqiues and Applications (DICTA) (2 Dec 2019 - 4 Dec 2019 : Perth, Australia)
Statement of
Rafael Felix, Ben Harwood, Michele Sasdelli and Gustavo Carneiro
Abstract: Generalised zero-shot learning (GZSL) is a classification problem where the learning stage relies on a set of seen visual classes and the inference stage aims to identify both the seen visual classes and a new set of unseen visual classes. Critically, both the learning and inference stages can leverage a semantic representation that is available for the seen and unseen classes. Most state-of-the-art GZSL approaches rely on a mapping between latent visual and semantic spaces without considering if a particular sample belongs to the set of seen or unseen classes. In this paper, we propose a novel GZSL method that learns a joint latent representation that combines both visual and semantic information. This mitigates the need for learning a mapping between the two spaces. Our method also introduces a domain classification that estimates whether a sample belongs to a seen or an unseen class. Our classifier then combines a class discriminator with this domain classifier with the goal of reducing the natural bias that GZSL approaches have toward the seen classes. Experiments show that our method achieves state-of-the-art results in terms of harmonic mean, the area under the seen and unseen curve and unseen classification accuracy on public GZSL benchmark data sets. Our code will be available upon acceptance of this paper.
Rights: © 2019 IEEE
DOI: 10.1109/DICTA47822.2019.8945949
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