3 accepted papers
User-guided colorization can predict the colors of a grayscale image according to user inputs, including exemplar images, local inputs and global inputs. Global inputs-based methods are probably the easiest ones to use, but are hard to distribute the input colors into correct regions, due to the lac…
Deep convolutional networks (CNNs) show their potential in image colorization for producing plausible results. Recently, the attention mechanism further boosts the performances of CNNs by constructing channel and spatial interactions. However, existing attention methods are performed in a single-sca…
Image colorization recovers a colorful image from a grayscale one. Trained by large-scale datasets, recent deep neural networks based methods can produce impressive colorful images. However, they usually directly train a single network using training images of fixed resolution. It is hard for such a…