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Lookup NU author(s): Dr Jie Su
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2023 by the authors. Existing zero-shot learning (ZSL) methods typically focus on mapping from the feature space (e.g., visual space) to class-level attributes, often leading to a non-injective projection. Such a mapping may cause a significant loss of instance-level information. While an ideal projection to instance-level attributes would be desirable, it can also be prohibitively expensive and thus impractical in many scenarios. In this work, we propose a variational disentangle zero-shot learning (VDZSL) framework that addresses this problem by constructing variational instance-specific attributes from a class-specific semantic latent distribution. Specifically, our approach disentangles each instance into class-specific attributes and the corresponding variant features. Unlike transductive ZSL, which assumes that unseen classes’ attributions are known beforehand, our VDZSL method does not rely on this strong assumption, making it more applicable in real-world scenarios. Extensive experiments conducted on three popular ZSL benchmark datasets (i.e., AwA2, CUB, and FLO) validate the effectiveness of our approach. In the conventional ZSL setting, our method demonstrates an improvement of 12∼15% relative to the advanced approaches and achieves a classification accuracy of 70% on the AwA2 dataset. Furthermore, under the more challenging generalized ZSL setting, our approach can gain an improvement of 5∼15% compared with the advanced methods.
Author(s): Su J, Wan J, Li T, Li X, Ye Y
Publication type: Article
Publication status: Published
Journal: Mathematics
Year: 2023
Volume: 11
Issue: 16
Online publication date: 18/08/2024
Acceptance date: 16/08/2024
Date deposited: 02/10/2024
ISSN (electronic): 2227-7390
Publisher: MDPI
URL: https://doi.org/10.3390/math11163578
DOI: 10.3390/math11163578
Data Access Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article.
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