Every Picture Tells a Story: Generating Sentences from Images
Abstract
Humans can prepare concise descriptions of pictures, focusing on what they find important. We demonstrate that automatic methods can do so too. We describe a system that can compute a score linking an image to a sentence. This score can be used to attach a descriptive sentence to a given image, or to obtain images that illustrate a given sentence. The score is obtained by comparing an estimate of meaning obtained from the image to one obtained from the sentence. Each estimate of meaning comes from a discriminative procedure that is learned using data. We evaluate on a novel dataset consisting of human-annotated images. While our underlying estimate of meaning is impoverished, it is sufficient to produce very good quantitative results, evaluated with a novel score that can account for synecdoche.
Cite
Text
Farhadi et al. "Every Picture Tells a Story: Generating Sentences from Images." European Conference on Computer Vision, 2010. doi:10.1007/978-3-642-15561-1_2Markdown
[Farhadi et al. "Every Picture Tells a Story: Generating Sentences from Images." European Conference on Computer Vision, 2010.](https://mlanthology.org/eccv/2010/farhadi2010eccv-every/) doi:10.1007/978-3-642-15561-1_2BibTeX
@inproceedings{farhadi2010eccv-every,
title = {{Every Picture Tells a Story: Generating Sentences from Images}},
author = {Farhadi, Ali and Hejrati, Seyyed Mohammad Mohsen and Sadeghi, Mohammad Amin and Young, Peter and Rashtchian, Cyrus and Hockenmaier, Julia and Forsyth, David A.},
booktitle = {European Conference on Computer Vision},
year = {2010},
pages = {15-29},
doi = {10.1007/978-3-642-15561-1_2},
url = {https://mlanthology.org/eccv/2010/farhadi2010eccv-every/}
}