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SADST:对于域名通用语义细分的风格意识的动态风格转移.
Jingxian Shen1, Jinlong Shi1, Jian Gu2
1School of Computer, Jiangsu University of Science and Technology, Zhenjiang, 212100, China.
概括
本研究介绍了域通用语义细分 (DGSS) 的风格意识动态风格转移 (SADST). SADST通过动态调整风格转移来改善模型概括,以保持跨域的语义信息.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 域通用语义细分 (DGSS) 模型在训练和未见域之间与风格变化 (纹理,照明) 斗争.
- 在DGSS中现有的风格转移方法经常导致过度风格化,导致语义信息丢失和概括性降低.
研究的目的:
- 开发一种新的方法,即风格意识的动态风格转移 (SADST),以增强DGSS模型的概括性.
- 解决DGSS中固定或随机风格转移策略的局限性.
主要方法:
- 风格提取块 (SEB) 从低级特征提取风格信息,同时保留语义线索.
- 动态风格传输模块 (DSTM) 根据原始和风格化特征预测风格传输强度.
- 风格-语义一致性损失通过在风格化特征中保持一致的细分结果来确保风格不变的表示.
主要成果:
- 萨德斯显著提高了DGSS的一般化表现.
- 拟议的方法优于目前最先进的DGSS技术.
- 实验证明了SADST在处理风格差异方面的有效性.
结论:
- 在语义细分中,SADST提供了一种更有效的方法来对域概括.
- 动态和风格意识的策略保留语义信息,从而在未见的领域获得更好的性能.
- 该方法为不同的视觉风格的现实应用提供了强大的解决方案.
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