Semantic-driven Colorization

Recent deep colorization works predict the semantic information implicitly while learning to colorize black-and-white photographic images. As a consequence, the generated color is easier to be overflowed, and the semantic faults are invisible. As human experience in coloring, the human first recognize which objects and their location in the photo, imagine which color is plausible for the objects as in real life, then colorize it. In this study, we simulate that human-like action to firstly let our network learn to segment what is in the photo, then colorize it. Therefore, our network can choose a plausible color under semantic constraint for specific objects, and give discriminative colors between them. Moreover, the segmentation map becomes understandable and interactable for the user. Our models are trained on PASCAL-Context and evaluated on selected images from the public domain and COCO-Stuff, which has several unseen categories compared to training data. As seen from the experimental results, our colorization system can provide plausible colors for specific objects and generate harmonious colors competitive with state-of-the-art methods.