论文标题
部分可观测时空混沌系统的无模型预测
iColoriT: Towards Propagating Local Hint to the Right Region in Interactive Colorization by Leveraging Vision Transformer
论文作者
论文摘要
当用户为特定位置提供颜色时，点相互作用的图像着色旨在使灰度图像着色。要在整个图像中适当地传播用户提供的颜色（即用户提示），以获取合理着色的图像，并以最小的用户工作来获得合理的图像，这一点至关重要。但是，由于堆叠卷积层的设计效率低下以传播到远处相关区域，因此现有方法通常会产生部分着色的结果。为了解决这个问题，我们提出了Icolorit，这是一种新型的点相互作用着色视觉变压器，能够传播到相关区域的用户提示，从而利用了变压器的全球接受场。变形金刚的自我发挥机制使ICOLORIT仅用几个局部提示来选择性地化色相关区域。我们的方法通过利用像素改组（一种取代解码器体系结构的有效的上采样技术）来实时着色。同样，为了减轻像素较大的上采样比的像素改组所致的工件，我们呈现局部稳定层。广泛的定量和定性结果表明，我们的方法高度优于现有的点相互作用的方法，从而用用户的努力最少产生精确着色的图像。官方代码可从https://pmh9960.github.io/research/icolorit获得
Point-interactive image colorization aims to colorize grayscale images when a user provides the colors for specific locations. It is essential for point-interactive colorization methods to appropriately propagate user-provided colors (i.e., user hints) in the entire image to obtain a reasonably colorized image with minimal user effort. However, existing approaches often produce partially colorized results due to the inefficient design of stacking convolutional layers to propagate hints to distant relevant regions. To address this problem, we present iColoriT, a novel point-interactive colorization Vision Transformer capable of propagating user hints to relevant regions, leveraging the global receptive field of Transformers. The self-attention mechanism of Transformers enables iColoriT to selectively colorize relevant regions with only a few local hints. Our approach colorizes images in real-time by utilizing pixel shuffling, an efficient upsampling technique that replaces the decoder architecture. Also, in order to mitigate the artifacts caused by pixel shuffling with large upsampling ratios, we present the local stabilizing layer. Extensive quantitative and qualitative results demonstrate that our approach highly outperforms existing methods for point-interactive colorization, producing accurately colorized images with a user's minimal effort. Official codes are available at https://pmh9960.github.io/research/iColoriT
