论文标题
在这里看!基于参数学习的方法,以重定向视觉注意力
Look here! A parametric learning based approach to redirect visual attention
论文作者
论文摘要
在整个摄影,营销和网站设计中,能够引起观众的注意是一种强大的工具。在专业工作流程中,我们引入了一种自动方法,使图像区域通过微妙的图像编辑更加捕捉图像区域,以维持现实主义和对原件的忠诚。从输入图像和用户提供的掩码中,我们的GazEshiftNet模型可预测将分别应用于前景和背景图像区域的一组不同的全局参数转换。我们介绍了定量和定性实验的结果,这些实验证明了对先前最新的改进。与现有的注意力转移算法相反,我们的全球参数方法更好地保留了图像语义,并避免了典型的生成伪像。我们的编辑能够以任何图像大小的交互速率推断,并可以轻松推广到视频。我们的模型的扩展可以进行多式编辑以及在图像区域增加和减轻注意力的能力。此外,用户可以通过参数空间中的插值向上或向下拨号来自定义编辑的图像。本文提出了一个实用的工具,可以简化未来的图像编辑管道。
Across photography, marketing, and website design, being able to direct the viewer's attention is a powerful tool. Motivated by professional workflows, we introduce an automatic method to make an image region more attention-capturing via subtle image edits that maintain realism and fidelity to the original. From an input image and a user-provided mask, our GazeShiftNet model predicts a distinct set of global parametric transformations to be applied to the foreground and background image regions separately. We present the results of quantitative and qualitative experiments that demonstrate improvements over prior state-of-the-art. In contrast to existing attention shifting algorithms, our global parametric approach better preserves image semantics and avoids typical generative artifacts. Our edits enable inference at interactive rates on any image size, and easily generalize to videos. Extensions of our model allow for multi-style edits and the ability to both increase and attenuate attention in an image region. Furthermore, users can customize the edited images by dialing the edits up or down via interpolations in parameter space. This paper presents a practical tool that can simplify future image editing pipelines.