Learning to Color from Language

Varun Manjunatha, Mohit Iyyer, Jordan Boyd-Graber, Larry Davis


Abstract
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.
Anthology ID:
N18-2120
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
764–769
Language:
URL:
https://www.aclweb.org/anthology/N18-2120
DOI:
10.18653/v1/N18-2120
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PDF:
http://aclanthology.lst.uni-saarland.de/N18-2120.pdf