@inproceedings{manjunatha-etal-2018-learning,
title = "Learning to Color from Language",
author = "Manjunatha, Varun and
Iyyer, Mohit and
Boyd-Graber, Jordan and
Davis, Larry",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2120",
doi = "10.18653/v1/N18-2120",
pages = "764--769",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Learning to Color from Language
%A Manjunatha, Varun
%A Iyyer, Mohit
%A Boyd-Graber, Jordan
%A Davis, Larry
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F manjunatha-etal-2018-learning
%X 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.
%R 10.18653/v1/N18-2120
%U https://aclanthology.org/N18-2120
%U https://doi.org/10.18653/v1/N18-2120
%P 764-769
Markdown (Informal)
[Learning to Color from Language](https://aclanthology.org/N18-2120) (Manjunatha et al., NAACL 2018)
ACL
- Varun Manjunatha, Mohit Iyyer, Jordan Boyd-Graber, and Larry Davis. 2018. Learning to Color from Language. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 764–769, New Orleans, Louisiana. Association for Computational Linguistics.