TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
A Novel Cross Channel Self-Attention based Approach for Facial Attribute Editing |
| 영문제목(English Title) |
A Novel Cross Channel Self-Attention based Approach for Facial Attribute Editing |
| 저자(Author) |
Meng Xu
Rize Jin
Liangfu Lu
Tae-Sun Chung
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| 원문수록처(Citation) |
VOL 15 NO. 06 PP. 2115 ~ 2127 (2021. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Although significant progress has been made in synthesizing visually realistic face images by Generative Adversarial Networks (GANs), there still lacks effective approaches to provide fine-grained control over the generation process for semantic facial attribute editing. In this work, we propose a novel cross channel self-attention based generative adversarial network (CCA-GAN), which weights the importance of multiple channels of features and archives pixel-level feature alignment and conversion, to reduce the impact on irrelevant attributes while editing the target attributes. Evaluation results show that CCA-GAN outperforms state-of-the-art models on the CelebA dataset, reducing Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) by 15~28% and 25~100%, respectively. Furthermore, visualization of generated samples confirms the effect of disentanglement of the proposed model. |
| 키워드(Keyword) |
Generative Adversarial Network
Cross Channel Self-Attention
Image Translation
Style Transfer
Facial Attribute Editing
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| 파일첨부 |
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