@inproceedings{ceb64a5aa0d74bf3bb4e999c8fda411d,
title = "Generative Wavelet-Multilayer Perception Feature Fusion Method for Zero-Shot Learning",
abstract = "For zero-shot learning, a Generative WaveletMultilayer Perception Feature Fusion (GWMPFF) method is proposed. In GWMPFF, the Generative Adversarial Networks(GAN) is used to generate virtual samples from unseen classes to alleviate the domain shift problem. Considering that the sensitivity of wavelet transform to contour information and high-frequency details is consistent with the characteristics of semantic description, a WaveletMultilayer Perceptron Feature Fusion (WMPFF) model is proposed to transform visual samples into semantic descriptions for classification. Finally, experiments on the standard datasets SUN and CUB demonstrate that the proposed method outperforms the comparison methods.",
keywords = "Feature fusion, Generative adversarial networks, Wavelet transform, Zero-shot learning",
author = "Zhu, \{Qun Xiong\} and Gao, \{Zi Shu\} and Ning Zhang and He, \{Yan Lin\} and Yuan Xu",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 2021 China Automation Congress, CAC 2021 ; Conference date: 22-10-2021 Through 24-10-2021",
year = "2021",
doi = "10.1109/CAC53003.2021.9727609",
language = "English",
series = "Proceeding - 2021 China Automation Congress, CAC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1233--1238",
booktitle = "Proceeding - 2021 China Automation Congress, CAC 2021",
address = "United States",
}