$\mathrm {CT^2}$: Colorization Transformer via Color Tokens.

Automatic image colorization is an ill-posed problem with multi-modal uncertainty, and there remains two main challenges with previous methods: incorrect semantic colors and under-saturation. In this paper, we propose an end-to-end transformer-based model to overcome these challenges. Benefited from the long-range context extraction of transformer and our holistic architecture, our method could colorize images with more diverse colors. Besides, we introduce color tokens into our approach and treat the colorization task as a classification problem, which increases the saturation of results. We also propose a series of modules to make image features interact with color tokens, and restrict the range of possible color candidates, which makes our results visually pleasing and reasonable. In addition, our method does not require any additional external priors, which ensures its well generalization capability. Extensive experiments and user studies demonstrate that our method achieves superior performance than previous works.