具有自适应感受野的中风可控快速风格转移

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摘要翻译：
最近提出了一种快速的风格转换方法，可以实时地将照片转换为艺术风格。这项任务包括控制程式化结果中的笔画大小，这仍然是一个开放的挑战。本文提出了一种笔画可控风格转换网络，可以实现连续和空间笔画大小的控制。通过分析影响笔画大小的因素，我们提出明确解释感受野和风格图像尺度。我们提出了一个StrokePyramid模块来赋予网络自适应的感受野，以及两种训练策略来实现更快的收敛和在训练模型上增加新的StrokePyramid模块。通过结合所提出的运行时控制策略，我们的网络可以实现笔划大小的连续变化，并在同一输出图像中的不同空间区域产生不同的笔划大小。
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英文标题：
《Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields》
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作者：
Yongcheng Jing, Yang Liu, Yezhou Yang, Zunlei Feng, Yizhou Yu, Dacheng
  Tao, Mingli Song
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最新提交年份：
2018
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  The Fast Style Transfer methods have been recently proposed to transfer a photograph to an artistic style in real-time. This task involves controlling the stroke size in the stylized results, which remains an open challenge. In this paper, we present a stroke controllable style transfer network that can achieve continuous and spatial stroke size control. By analyzing the factors that influence the stroke size, we propose to explicitly account for the receptive field and the style image scales. We propose a StrokePyramid module to endow the network with adaptive receptive fields, and two training strategies to achieve faster convergence and augment new stroke sizes upon a trained model respectively. By combining the proposed runtime control strategies, our network can achieve continuous changes in stroke sizes and produce distinct stroke sizes in different spatial regions within the same output image.
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PDF链接：
https://arxiv.org/pdf/1802.07101

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关键词：Controllable Presentation respectively Architecture Construction 转换 network 空间 spatial 中风