论文标题
DEEPCFL：深层上下文特征从单个图像中学习
DeepCFL: Deep Contextual Features Learning from a Single Image
论文作者
论文摘要
最近，人们对开发独立于培训数据的图像特征学习方法（例如Deep Image Prior，Ingan，Singan和DCIL）引起了极大的兴趣。这些方法是无监督的，用于执行低级视觉任务，例如图像恢复，图像编辑和图像合成。在这项工作中，我们提出了一个新的与数据独立的框架，称为“深层上下文特征学习”（DEEPCFL），以根据输入图像的语义执行图像合成和图像恢复。上下文特征只是代表给定图像的语义的高维向量。 DEEPCFL是一个单个图像gan框架，它从输入图像中学习上下文向量的分布。我们在各种具有挑战性的场景中显示了上下文学习的表现：主修，介绍和恢复随机删除的像素。当输入源图像和生成的目标图像不对齐时，DEEPCFL适用。我们使用DEEPCFL来说明图像调整大小的任务。
Recently, there is a vast interest in developing image feature learning methods that are independent of the training data, such as deep image prior, InGAN, SinGAN, and DCIL. These methods are unsupervised and are used to perform low-level vision tasks such as image restoration, image editing, and image synthesis. In this work, we proposed a new training data-independent framework, called Deep Contextual Features Learning (DeepCFL), to perform image synthesis and image restoration based on the semantics of the input image. The contextual features are simply the high dimensional vectors representing the semantics of the given image. DeepCFL is a single image GAN framework that learns the distribution of the context vectors from the input image. We show the performance of contextual learning in various challenging scenarios: outpainting, inpainting, and restoration of randomly removed pixels. DeepCFL is applicable when the input source image and the generated target image are not aligned. We illustrate image synthesis using DeepCFL for the task of image resizing.
