双域风格的传输网络是双域式的
Changyang Hu1, Shibao Sun1, Pengcheng Zhao1
1College of the software, Henan University of Science and Technology, Luoyang, China.
Science progress
|May 7, 2025
概括
本研究介绍了一种双域风格转移网络,以减少任意风格转移中的文物. 这种新的方法增强了风格语义和全局纹理,以提高图像质量.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 随意的风格转移正在因其多样化的应用而获得吸引力.
- 当前的方法往往导致文物和糟糕的纹理,由于有限的语义理解和远程依赖性捕获.
研究的目的:
- 开发一种先进的任意风格转移方法,将文物最小化并提高纹理质量.
- 通过探索风格语义分布和远程依赖来解决现有方法的局限性.
主要方法:
- 引入了一种双域风格传输网络.
- 整合了适应性规范化与风格语义意识使用自我注意力.
- 在频率领域实现了全球风格纹理增强.
主要成果:
- 在MSCOCO和Wikiart数据集上实现了最先进的性能.
- 在学习感知图像补丁相似性 (0.616),结构相似性指数 (0.467) 和内容丢失 (2.31) 中获得最高分.
- 在风格损失 (3.08) 中获得第二个最佳分数.
结论:
- 拟议的双域风格传输网络有效地减少了文物并提高了纹理质量.
- 风格语义意识和频率域增强的整合为任意风格转移提供了显著的进步.
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