首页|Unsupervised multi-modal image translation based on the squeeze-and-excitation mechanism and feature attention module

Unsupervised multi-modal image translation based on the squeeze-and-excitation mechanism and feature attention module

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  • 万方数据
  • 维普
The unsupervised multi-modal image translation is an emerging domain of computer vision whose goal is to transform an image from the source domain into many diverse styles in the target do-main.However,the multi-generator mechanism is employed among the advanced approaches availa-ble to model different domain mappings,which results in inefficient training of neural networks and pattern collapse,leading to inefficient generation of image diversity.To address this issue,this paper introduces a multi-modal unsupervised image translation framework that uses a generator to perform multi-modal image translation.Specifically,firstly,the domain code is introduced in this paper to explicitly control the different generation tasks.Secondly,this paper brings in the squeeze-and-exci-tation(SE)mechanism and feature attention(FA)module.Finally,the model integrates multiple optimization objectives to ensure efficient multi-modal translation.This paper performs qualitative and quantitative experiments on multiple non-paired benchmark image translation datasets while demon-strating the benefits of the proposed method over existing technologies.Overall,experimental results have shown that the proposed method is versatile and scalable.

multi-modal image translationgenerative adversarial network(GAN)squeeze-and-excitation(SE)mechanismfeature attention(FA)module

HU Zhentao(胡振涛)、HU Chonghao、YANG Haoran、SHUAI Weiwei

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School of Artificial Intelligence,Henan University,Zhengzhou 450046,P.R.China

95795 Troops of the PLA,Guilin 541003,P.R.China

National Natural Science Foundation of ChinaAcademic Degrees&Graduate Education Reform Project of Henan ProvinceTeaching Reform Research and Practice Project of Henan Undergraduate UniversitiesKey Project on Research and Practice of Henan University Graduate Education and Teaching Reform

619760802021SJGLX195Y2022SYJXLX008YJSJG2023XJ006

2024

10.3772/j.issn.1006-6748.2024.01.003

高技术通讯(英文版)
中国科学技术信息研究所（ISTIC）

高技术通讯(英文版)

影响因子：0.058
ISSN：1006-6748
年,卷(期)：2024.30(1)
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