Semantics Disentangling for Generalized Zero-Shot Learning

Paper

Chen, Zhi, Luo, Yadan, Qui, Ruihong, Wang, Sen, Huang, Zi, Li, Jingjing and Zhang, Zheng. 2022. "Semantics Disentangling for Generalized Zero-Shot Learning." 2021 IEEE/CVF International Conference on Computer Vision (ICCV). Montreal, Canada 10 - 17 Oct 2021 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/ICCV48922.2021.00859
Paper/Presentation Title

Semantics Disentangling for Generalized Zero-Shot Learning

Presentation TypePaper
AuthorsChen, Zhi, Luo, Yadan, Qui, Ruihong, Wang, Sen, Huang, Zi, Li, Jingjing and Zhang, Zheng
Journal or Proceedings TitleProceedings of 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Journal Citationpp. 8692-8700
Number of Pages9
Year2022
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISBN9781665428125
Digital Object Identifier (DOI)https://doi.org/10.1109/ICCV48922.2021.00859
Web Address (URL) of Paperhttps://ieeexplore.ieee.org/document/9710280
Web Address (URL) of Conference Proceedingshttps://ieeexplore.ieee.org/xpl/conhome/9709627/proceeding
Conference/Event2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Event Details
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Delivery
In person
Event Date
10 to end of 17 Oct 2021
Event Location
Montreal, Canada
Abstract

Generalized zero-shot learning (GZSL) aims to classify samples under the assumption that some classes are not observable during training. To bridge the gap between the seen and unseen classes, most GZSL methods attempt to associate the visual features of seen classes with attributes or to generate unseen samples directly. Nevertheless, the visual features used in the prior approaches do not necessarily encode semantically related information that the shared attributes refer to, which degrades the model generalization to unseen classes. To address this issue, in this paper, we propose a novel semantics disentangling framework for the generalized zero-shot learning task (SDGZSL), where the visual features of unseen classes are firstly estimated by a conditional VAE and then factorized into semantic-consistent and semantic-unrelated latent vectors. In particular, a total correlation penalty is applied to guarantee the independence between the two factorized representations, and the semantic consistency of which is measured by the derived relation network. Extensive experiments conducted on four GZSL benchmark datasets have evidenced that the semantic-consistent features disentangled by the proposed SDGZSL are more generalizable in tasks of canonical and generalized zero-shot learning. Our source code is available at https://github.com/uqzhichen/SDGZSL.

Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204602. Artificial intelligence
Public Notes

Semantics Disentangling for Generalized Zero-Shot Learning

Byline AffiliationsUniversity of Queensland
University of Electronic Science and Technology of China, China
Harbin Institute of Technology, China
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