TY - GEN
T1 - Story visualization using image-text matching architecture for digital storytelling
AU - Yturrizaga-Aguirre, Arian
AU - Silva-Olivares, Camilo
AU - Ugarte, Willy
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Currently, the techniques for generating images from text used to visualize stories have serious limitations in terms of image quality, which prevents quantifying their impact in real life scenarios. An example of this occurs in the field of education, where digital storytelling is used as a tool to incite teaching. For this reason, we propose to design a web interface that allows primary school children to write a short story and obtain, as a result, a sequence of coherent and representative images of said content, emulating a conventional process of educational digital storytelling. We describe the use of an Image-text matching architecture based on NLP and Image Retrieval for the story visualization task focused on digital storytelling. To evaluate the performance of the architecture, the quantitative metrics: WuPalmer and cosine similarity were used, in addition to qualitative metrics.
AB - Currently, the techniques for generating images from text used to visualize stories have serious limitations in terms of image quality, which prevents quantifying their impact in real life scenarios. An example of this occurs in the field of education, where digital storytelling is used as a tool to incite teaching. For this reason, we propose to design a web interface that allows primary school children to write a short story and obtain, as a result, a sequence of coherent and representative images of said content, emulating a conventional process of educational digital storytelling. We describe the use of an Image-text matching architecture based on NLP and Image Retrieval for the story visualization task focused on digital storytelling. To evaluate the performance of the architecture, the quantitative metrics: WuPalmer and cosine similarity were used, in addition to qualitative metrics.
KW - Deep Learning
KW - Story Generative model
UR - https://www.scopus.com/pages/publications/85142851808
U2 - 10.1109/EIRCON56026.2022.9934817
DO - 10.1109/EIRCON56026.2022.9934817
M3 - Contribución a la conferencia
AN - SCOPUS:85142851808
T3 - Proceedings of the 2022 IEEE Engineering International Research Conference, EIRCON 2022
BT - Proceedings of the 2022 IEEE Engineering International Research Conference, EIRCON 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Engineering International Research Conference, EIRCON 2022
Y2 - 26 October 2022 through 28 October 2022
ER -