论文标题
通过逐步发展面具区域来学习涂料
Learning to Inpaint by Progressively Growing the Mask Regions
论文作者
论文摘要
图像介绍是计算机视觉中最具挑战性的任务之一。最近，已经证明基于生成的图像授课方法可以产生视觉上合理的图像。但是，随着蒙面区域的增长，它们仍然很难生成正确的结构和颜色。这个缺点是由于生成模型的训练稳定性问题。这项工作在图像介入的背景下引入了一种新的课程式培训方法。所提出的方法在训练时间内逐渐增加了蒙版区域的大小，在测试时间内，用户在任意位置提供了可变的大小和多个孔。将这种方法纳入gan可以稳定训练，并提供更好的颜色一致性并捕获对象连续性。我们在MSCOCO和CELEBA数据集上验证我们的方法。我们报告了不同模型中培训方法的定性和定量比较。
Image inpainting is one of the most challenging tasks in computer vision. Recently, generative-based image inpainting methods have been shown to produce visually plausible images. However, they still have difficulties to generate the correct structures and colors as the masked region grows large. This drawback is due to the training stability issue of the generative models. This work introduces a new curriculum-style training approach in the context of image inpainting. The proposed method increases the masked region size progressively in training time, during test time the user gives variable size and multiple holes at arbitrary locations. Incorporating such an approach in GANs may stabilize the training and provides better color consistencies and captures object continuities. We validate our approach on the MSCOCO and CelebA datasets. We report qualitative and quantitative comparisons of our training approach in different models.
