Face attribute editing network based on style-content disentanglement and convolutional attention

Jiansheng Cui; Quansheng Dou

doi:10.1117/12.2685479

15 August 2023 Face attribute editing network based on style-content disentanglement and convolutional attention

Jiansheng Cui, Quansheng Dou

Proceedings Volume 12719, Second International Conference on Electronic Information Technology (EIT 2023); 1271911 (2023) https://doi.org/10.1117/12.2685479
Event: Second International Conference on Electronic Information Technology (EIT 2023), 2023, Wuhan, China

Abstract

Face attribute editing is a research hotspot in the field of computer vision, which aims to modify a certain attribute of a face image to generate a new face image. The current methods based on Generative Adversarial Networks (GAN) have attribute entanglement problems and the implementation process is relatively complicated. To this end, this paper proposes a face attribute editing network based on style-content disentanglement and convolutional attention. Adding convolutional attention (CAT) module to the StyleGAN generator makes the network's control of content features no longer affected by the overall style of the image, and realizes the separation of spatial content and style from coarse to fine. In addition, the hierarchical CAT modules control different levels of attribute features, and changing the input of any layer of CAT can change the corresponding attribute features. The experimental results on the CelebA-HQ dataset show that the method in this paper can achieve disentangled editing of face attributes, and the scores of various indicators are better than the existing models.

Citation Download Citation

Jiansheng Cui and Quansheng Dou "Face attribute editing network based on style-content disentanglement and convolutional attention", Proc. SPIE 12719, Second International Conference on Electronic Information Technology (EIT 2023), 1271911 (15 August 2023); https://doi.org/10.1117/12.2685479

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KEYWORDS

Computed tomography

Convolution

Image quality

Interpolation

Education and training

Image processing

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