相关实验视频
SPAST:通过预先训练的大型模型,随意转移风格与风格先验
Zhanjie Zhang1, Quanwei Zhang1, Junsheng Luan1
1College of Computer Science and Technology, Zhejiang University, No. 38, Zheda Road, Hangzhou 310000, China.
概括
这项研究介绍了SPAST,一种用于任意风格转移的新框架. 它通过保持内容结构和减少推断时间,高效地生成高质量的风格图像.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 任意风格转移旨在将一个图像的内容结构与另一个图像的风格相结合.
- 使用小模型的现有方法产生低质量的结果,而大模型是缓慢的,可能会改变内容结构.
研究的目的:
- 开发一种新的框架,SPAST,用于高质量的任意风格转移,减少推理时间.
- 在图像质量,内容保存和计算效率方面解决现有方法的局限性.
主要方法:
- 提出了一个新的本地-全球窗口尺寸造型模块 (LGWSSM),以有效地融合风格和内容特征.
- 引入了损失前的风格,以利用来自大型预训练模型的知识,提高SPAST的性能.
- 开发了一个名为SPAST的新框架,用于高效的任意风格转移.
主要成果:
- SPAST生成高质量的风格图像,有效地保留内容结构.
- 与最先进的技术相比,拟议的方法显著减少了推断时间.
- 实验结果验证了SPAST在任意风格转移中的有效性.
结论:
- SPAST提供了一种优越的方法来任意转移风格,平衡图像质量,内容忠实性和速度.
- 新的LGWSSM和风格前损失是使高效和高质量的造型成为可能的关键组成部分.
- 这项工作通过为任意风格转移提供实用和有效的解决方案,推动了该领域的发展.
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