高保真性和高效的多元化图像完成与变压器
概括
这项研究介绍了一种新的图像完成方法,将变压器和CNN结合起来,以增强结构和纹理. 一个新的解码策略,TAPS,显著加快推断,同时保持高质量,多样化的结果.
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:
- 卷积神经网络 (CNN) 在纹理建模方面表现出色,但在全球结构和多元化图像完成方面扎.
- 变压器有效地模拟远程依赖关系并产生多样化的输出,但在高分辨率图像方面面临计算挑战.
- 现有的变压器自动回归采样对于解码各种图像完成结果是低效的.
研究的目的:
- 开发一种有效和高效的方法,用于多元化的图像完成.
- 将变压器和CNN的优势结合起来,实现卓越的图像重建.
- 为基于变压器的图像完成引入更快的解码策略.
主要方法:
- 一种使用变压器进行外观前重建和CNN进行纹理补充的混合方法.
- 实施一种新的解码策略,即温度回火概率采样 (TAPS),以进行高效的推断.
- 设计一种纹理感知指导注意模块,以提高现实性和解决边界文物.
主要成果:
- 拟议的方法在图像保真性和多元化完成质量方面明显优于最先进的方法.
- 在保持高质量和多样性的同时,TAPS实现了超过70倍的推理速度.
- 该方法在像ImageNet.Net这样的大型面具和数据集上表现出异常的概括性.
- 纹理感知引导注意模块增强了现实主义和连贯性,减轻了边界工件.
结论:
- 混合变压器-CNN方法为多元化图像完成提供了强大的解决方案.
- TAPS为基于变压器的图像生成提供了显著的效率提高.
- 开发的方法在图像完成任务中实现了卓越的性能,多样性和概括能力.
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