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Triple Verification Network for Generalised Zero-shot Learning

Lookup NU author(s): Dr Yang Long, Dr Yu GuanORCiD



This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2018.

For re-use rights please refer to the publisher's terms and conditions.


IEEE Conventional Zero-shot Learning approaches often suffer from severe performance degradation in the Generalised Zero-shot Learning (GZSL) scenario, i.e. to recognise test images that are from both seen and unseen classes. This paper studies the Class-level Over-fitting (CO) and empirically shows its effects to GZSL. We then address ZSL as a Triple Verification problem and propose a unified optimisation of regression and compatibility functions, i.e. two main streams of existing ZSL approaches. The complementary losses mutually regularise the same model to mitigate the CO problem. Furthermore, we implement a deep extension paradigm to linear models and significantly outperforms state-of-the-art methods in both GZSL and ZSL scenarios on the four standard benchmarks.

Publication metadata

Author(s): Zhang H, Long Y, Guan Y, Shao L

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Image Processing

Year: 2018

Volume: 28

Issue: 1

Pages: 506-517

Print publication date: 01/01/2019

Online publication date: 24/09/2018

Acceptance date: 03/09/2018

Date deposited: 17/01/2019

ISSN (print): 1057-7149

ISSN (electronic): 1941-0042

Publisher: IEEE


DOI: 10.1109/TIP.2018.2869696


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