论文标题
文字引导的无面膜的本地图像修饰
Text-Guided Mask-free Local Image Retouching
论文作者
论文摘要
在多模式的领域，随着深度学习的出现，出现了文本引导的图像修饰技术。但是，大多数当前可用的文本指导方法依赖于对象级的监督来限制可能被修改的区域。这不仅使开发这些算法更具挑战性，而且还限制了如何将深度学习用于图像修饰。在本文中，我们提供了一种不含文本的无面膜图像修饰方法，可以产生一致的结果来解决此问题。为了在没有掩盖监督的情况下执行图像修饰，我们的技术可以根据图像中每个对象的文本来构建合理的边缘掩模。广泛的实验表明，我们的方法可以根据口语产生高质量的准确图像。源代码将很快发布。
In the realm of multi-modality, text-guided image retouching techniques emerged with the advent of deep learning. Most currently available text-guided methods, however, rely on object-level supervision to constrain the region that may be modified. This not only makes it more challenging to develop these algorithms, but it also limits how widely deep learning can be used for image retouching. In this paper, we offer a text-guided mask-free image retouching approach that yields consistent results to address this concern. In order to perform image retouching without mask supervision, our technique can construct plausible and edge-sharp masks based on the text for each object in the image. Extensive experiments have shown that our method can produce high-quality, accurate images based on spoken language. The source code will be released soon.
