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M2C: Towards Automatic Multimodal Manga Complement

Hongcheng Guo, Boyang Wang, Jiaqi Bai, Jiaheng Liu, Jian Yang, Zhoujun Li


Abstract
Multimodal manga analysis focuses on enhancing manga understanding with visual and textual features, which has attracted considerable attention from both natural language processing and computer vision communities. Currently, most comics are hand-drawn and prone to problems such as missing pages, text contamination, and text aging, resulting in missing comic text content and seriously hindering human comprehension. In other words, the Multimodal Manga Complement (M2C) task has not been investigated, which aims to handle the aforementioned issues by providing a shared semantic space for vision and language understanding. To this end, we first propose the Multimodal Manga Complement task by establishing a new M2C benchmark dataset covering two languages. First, we design a manga argumentation method called MCoT to mine event knowledge in comics with large language models. Then, an effective baseline FVP-M2 using fine-grained visual prompts is proposed to support manga complement. Extensive experimental results show the effectiveness of FVP-M2 method for Multimodal Mange Complement.
Anthology ID:
2023.findings-emnlp.661
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9876–9882
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.661
DOI:
10.18653/v1/2023.findings-emnlp.661
Bibkey:
Cite (ACL):
Hongcheng Guo, Boyang Wang, Jiaqi Bai, Jiaheng Liu, Jian Yang, and Zhoujun Li. 2023. M2C: Towards Automatic Multimodal Manga Complement. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 9876–9882, Singapore. Association for Computational Linguistics.
Cite (Informal):
M2C: Towards Automatic Multimodal Manga Complement (Guo et al., Findings 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.findings-emnlp.661.pdf