Learning to Model Multimodal Semantic Alignment for Story Visualization

Bowen Li, Thomas Lukasiewicz


Abstract
Story visualization aims to generate a sequence of images to narrate each sentence in a multi-sentence story, where the images should be realistic and keep global consistency across dynamic scenes and characters. Current works face the problem of semantic misalignment because of their fixed architecture and diversity of input modalities. To address this problem, we explore the semantic alignment between text and image representations by learning to match their semantic levels in the GAN-based generative model. More specifically, we introduce dynamic interactions according to learning to dynamically explore various semantic depths and fuse the different-modal information at a matched semantic level, which thus relieves the text-image semantic misalignment problem. Extensive experiments on different datasets demonstrate the improvements of our approach, neither using segmentation masks nor auxiliary captioning networks, on image quality and story consistency, compared with state-of-the-art methods.
Anthology ID:
2022.findings-emnlp.346
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4712–4718
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.346
DOI:
10.18653/v1/2022.findings-emnlp.346
Bibkey:
Cite (ACL):
Bowen Li and Thomas Lukasiewicz. 2022. Learning to Model Multimodal Semantic Alignment for Story Visualization. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 4712–4718, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Learning to Model Multimodal Semantic Alignment for Story Visualization (Li & Lukasiewicz, Findings 2022)
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PDF:
https://aclanthology.org/2022.findings-emnlp.346.pdf