L-CoDe:Language-Based Colorization Using Color-Object Decoupled Conditions
| Autor: | Shuchen Weng, Hao Wu, Zheng Chang, Jiajun Tang, Si Li, Boxin Shi |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Proceedings of the AAAI Conference on Artificial Intelligence. 36:2677-2684 |
| ISSN: | 2374-3468 2159-5399 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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