Puff-Net: Efficient Style Transfer with Pure Content and Style Feature Fusion Network
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arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916265875996672 |
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| author | Zheng, Sizhe Gao, Pan Zhou, Peng Qin, Jie |
| author_facet | Zheng, Sizhe Gao, Pan Zhou, Peng Qin, Jie |
| contents | Style transfer aims to render an image with the artistic features of a style image, while maintaining the original structure. Various methods have been put forward for this task, but some challenges still exist. For instance, it is difficult for CNN-based methods to handle global information and long-range dependencies between input images, for which transformer-based methods have been proposed. Although transformers can better model the relationship between content and style images, they require high-cost hardware and time-consuming inference. To address these issues, we design a novel transformer model that includes only the encoder, thus significantly reducing the computational cost. In addition, we also find that existing style transfer methods may lead to images under-stylied or missing content. In order to achieve better stylization, we design a content feature extractor and a style feature extractor, based on which pure content and style images can be fed to the transformer. Finally, we propose a novel network termed Puff-Net, i.e., pure content and style feature fusion network. Through qualitative and quantitative experiments, we demonstrate the advantages of our model compared to state-of-the-art ones in the literature. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_19775 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Puff-Net: Efficient Style Transfer with Pure Content and Style Feature Fusion Network Zheng, Sizhe Gao, Pan Zhou, Peng Qin, Jie Computer Vision and Pattern Recognition Style transfer aims to render an image with the artistic features of a style image, while maintaining the original structure. Various methods have been put forward for this task, but some challenges still exist. For instance, it is difficult for CNN-based methods to handle global information and long-range dependencies between input images, for which transformer-based methods have been proposed. Although transformers can better model the relationship between content and style images, they require high-cost hardware and time-consuming inference. To address these issues, we design a novel transformer model that includes only the encoder, thus significantly reducing the computational cost. In addition, we also find that existing style transfer methods may lead to images under-stylied or missing content. In order to achieve better stylization, we design a content feature extractor and a style feature extractor, based on which pure content and style images can be fed to the transformer. Finally, we propose a novel network termed Puff-Net, i.e., pure content and style feature fusion network. Through qualitative and quantitative experiments, we demonstrate the advantages of our model compared to state-of-the-art ones in the literature. |
| title | Puff-Net: Efficient Style Transfer with Pure Content and Style Feature Fusion Network |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.19775 |