Generalized zero-shot learning based on dual latent space reconstruction

Yangdongfang Xu; Guan Yang; Xiaoming Liu; Yang Liu

doi:10.1117/12.2652464

6 September 2022 Generalized zero-shot learning based on dual latent space reconstruction

Yangdongfang Xu, Guan Yang, Xiaoming Liu, Yang Liu

Proceedings Volume 12332, International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2022); 123321V (2022) https://doi.org/10.1117/12.2652464
Event: International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2022), 2022, Chengdu, China

Abstract

Generalized Zero-Shot Learning (GZSL) is characterized as a training process that comprises visual samples from seen classes and semantic samples from seen and unseen classes, followed by a testing process that classifies visual samples from seen and unseen classes. Existing zero-shot learning (ZSL) approaches suffer from domain shift and information loss issues as a result of class differences between visible and unseen classes, as well as uneven image distribution. In this study, a generalized zero-shot learning strategy based on dual latent space reconstruction (DLR-GZSL) is proposed. The method aims to establish a latent space of shared semantic and visual information, uses dual learning to align different modal representations to alleviate the domain shift problem, uses triplet loss to improve intra-class diversity and inter-class separability of the generated samples, and uses information bottleneck to retain as much valid generated feature information as possible to reduce information loss. Experiments on the CUB, SUN, AWA1, and AWA2 datasets reveal that the suggested method has more accurate than previous methods, demonstrating its effectiveness.

Citation Download Citation

Yangdongfang Xu, Guan Yang, Xiaoming Liu, and Yang Liu "Generalized zero-shot learning based on dual latent space reconstruction", Proc. SPIE 12332, International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2022), 123321V (6 September 2022); https://doi.org/10.1117/12.2652464

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KEYWORDS

Data modeling

Visualization

Visual process modeling

Statistical modeling

Computer programming

Classification systems

Computer vision technology

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