A Novel Cross Channel Self-Attention based Approach for Facial Attribute Editing
Meng Xu, Rize Jin, Liangfu Lu, Tae‐Sun Chung · KSII Transactions on Internet and Information Systems · 2021
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 stateof-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.