Enhanced VAEGAN: a zero-shot image classification method

Ding, Bo; Ye, Fan; He, Yongjun; Zhao, Jing

doi:10.1007/s10489-022-03869-7

Cited by 1 publication

(1 citation statement)

References 36 publications

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“…The decoder of MVAE is responsible for reconstructing latent features into corresponding semantic information and ensuring the separability of category semantic information through a cross‐modal reconstruction loss. Similarly, Ding et al (2022) also adopted the combined framework of VAE and GAN to solve the zero‐shot learning problem. Different from Ma et al, they fused visual features, attribute features, and hidden layer features in the feature alignment module, providing the encoder with more information.…”

Section: Representative Algorithmsmentioning

confidence: 99%

A review on multimodal zero‐shot learning

Cao

Sun

et al. 2023

WIREs Data Min & Knowl

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Multimodal learning provides a path to fully utilize all types of information related to the modeling target to provide the model with a global vision. Zero-shot learning (ZSL) is a general solution for incorporating prior knowledge into data-driven models and achieving accurate class identification. The combination of the two, known as multimodal ZSL (MZSL), can fully exploit the advantages of both technologies and is expected to produce models with greater generalization ability. However, the MZSL algorithms and applicationshave not yet been thoroughly investigated and summarized. This study fills this gap by providing an objective overview of MZSL's definition, typical algorithms, representative applications, and critical issues. This article will not only provide researchers in this field with a comprehensive perspective, but it will also highlight several promising research directions.

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Section: Representative Algorithmsmentioning

confidence: 99%