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Learning to Color from Language
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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. Through this language-based framework, we can dramatically alter colorizations by manipulating descriptive color words in captions.
Forward citations
Cited by 2 Pith papers
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Enabling Region-Specific Control via Lassos in Point-Based Colorization
A lasso-guided attention mask in a transformer colorization model reduces color collapse and cuts user interaction effort by roughly 30%.
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ColorFlow: Retrieval-Augmented Image Sequence Colorization
ColorFlow is a three-stage diffusion framework that colorizes black-and-white image sequences while preserving character and object color identity via retrieved reference patches.
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