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
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 764–769
- Language:
- URL:
- https://aclanthology.org/N18-2120
- DOI:
- 10.18653/v1/N18-2120
- Cite (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.
- Cite (Informal):
- Learning to Color from Language (Manjunatha et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/N18-2120.pdf
- Code
- superhans/colorfromlanguage
- Data
- MS COCO