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Ȩ > ¿¬±¸¹®Çå > ¿µ¹® ³í¹®Áö >
TIIS (Çѱ¹ÀÎÅͳÝÁ¤º¸ÇÐȸ)
TIIS (Çѱ¹ÀÎÅͳÝÁ¤º¸ÇÐȸ)
Current Result Document :
1
/ 2
´ÙÀ½°Ç
ÇѱÛÁ¦¸ñ(Korean Title)
A Novel Cross Channel Self-Attention based Approach for Facial Attribute Editing
¿µ¹®Á¦¸ñ(English Title)
A Novel Cross Channel Self-Attention based Approach for Facial Attribute Editing
ÀúÀÚ(Author)
Meng Xu
Rize Jin
Liangfu Lu
Tae-Sun Chung
¿ø¹®¼ö·Ïó(Citation)
VOL 15 NO. 06 PP. 2115 ~ 2127 (2021. 06)
Çѱ۳»¿ë
(Korean Abstract)
¿µ¹®³»¿ë
(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.
Å°¿öµå(Keyword)
Generative Adversarial Network
Cross Channel Self-Attention
Image Translation
Style Transfer
Facial Attribute Editing
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