@article{Weng_Wu_Chang_Tang_Li_Shi_2022, title={L-CoDe:Language-Based Colorization Using Color-Object Decoupled Conditions}, volume={36}, url={https://ojs.aaai.org/index.php/AAAI/article/view/20170}, DOI={10.1609/aaai.v36i3.20170}, abstractNote={Colorizing a grayscale image is inherently an ill-posed problem with multi-modal uncertainty. Language-based colorization offers a natural way of interaction to reduce such uncertainty via a user-provided caption. However, the color-object coupling and mismatch issues make the mapping from word to color difficult. In this paper, we propose L-CoDe, a Language-based Colorization network using color-object Decoupled conditions. A predictor for object-color corresponding matrix (OCCM) and a novel attention transfer module (ATM) are introduced to solve the color-object coupling problem. To deal with color-object mismatch that results in incorrect color-object correspondence, we adopt a soft-gated injection module (SIM). We further present a new dataset containing annotated color-object pairs to provide supervisory signals for resolving the coupling problem. Experimental results show that our approach outperforms state-of-the-art methods conditioned on captions.}, number={3}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Weng, Shuchen and Wu, Hao and Chang, Zheng and Tang, Jiajun and Li, Si and Shi, Boxin}, year={2022}, month={Jun.}, pages={2677-2684} }