APA
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Cho, Y., Lee, J., Yang, S., Kim, J., Park, Y., Lee, H., … Choo, J. (2022). Guiding Users to Where to Give Color Hints for Efficient Interactive Sketch Colorization via Unsupervised Region Prioritization. IEEE Workshop/Winter Conference on Applications of Computer Vision.
Chicago/Turabian
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Cho, Youngin, Junsoo Lee, Soyoung Yang, Juntae Kim, Yeojeong Park, Haneol Lee, Mohammad Azam Khan, Daesik Kim, and J. Choo. “Guiding Users to Where to Give Color Hints for Efficient Interactive Sketch Colorization via Unsupervised Region Prioritization.” IEEE Workshop/Winter Conference on Applications of Computer Vision (2022).
MLA
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Cho, Youngin, et al. “Guiding Users to Where to Give Color Hints for Efficient Interactive Sketch Colorization via Unsupervised Region Prioritization.” IEEE Workshop/Winter Conference on Applications of Computer Vision, 2022.
BibTeX Click to copy
@article{youngin2022a,
title = {Guiding Users to Where to Give Color Hints for Efficient Interactive Sketch Colorization via Unsupervised Region Prioritization},
year = {2022},
journal = {IEEE Workshop/Winter Conference on Applications of Computer Vision},
author = {Cho, Youngin and Lee, Junsoo and Yang, Soyoung and Kim, Juntae and Park, Yeojeong and Lee, Haneol and Khan, Mohammad Azam and Kim, Daesik and Choo, J.}
}
Existing deep interactive colorization models have focused on ways to utilize various types of interactions, such as point-wise color hints, scribbles, or natural-language texts, as methods to reflect a user’s intent at runtime. However, another approach, which actively informs the user of the most effective regions to give hints for sketch image colorization, has been under-explored. This paper proposes a novel model-guided deep interactive colorization framework that reduces the required amount of user interactions, by prioritizing the regions in a colorization model. Our method, called GuidingPainter, prioritizes these regions where the model most needs a color hint, rather than just relying on the user’s manual decision on where to give a color hint. In our extensive experiments, we show that our approach outperforms existing interactive colorization methods in terms of the conventional metrics, such as PSNR and FID, and reduces required amount of interactions.
