Learning to Color from Language
Learning to Color from Language
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DOI：
10.18653/v1/n18-2120
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发表时间：
2018-04
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影响因子：
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通讯作者：
Varun Manjunatha;Mohit Iyyer;Jordan L. Boyd-Graber;L. Davis
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文献类型：
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作者：
Varun Manjunatha;Mohit Iyyer;Jordan L. Boyd-Graber;L. Davis
自动彩色化是将颜色添加到灰度图像的过程。我们根据语言来调节这个过程，允许最终用户通过输入不同的标题来操纵彩色图像。我们提出了两种不同的语言条件着色体系结构，这两种体系结构都比语言不可知版本产生更准确和更合理的着色。此外，我们通过众包实验证明，我们可以通过操纵标题中的描述性颜色词来显着改变着色。
Automatic colorization is the process of adding color to greyscale images. We condition this process on language, allowing end users to manipulate a colorized image by feeding in different captions. We present two different architectures for language-conditioned colorization, both of which produce more accurate and plausible colorizations than a language-agnostic version. Furthermore, we demonstrate through crowdsourced experiments that we can dramatically alter colorizations simply by manipulating descriptive color words in captions.