ACL 2022findings45 citations
Modular and Parameter-Efficient Multimodal Fusion with Prompting
Sheng Liang, Mengjie Zhao, Hinrich Schuetze
Abstract
Recent research has made impressive progress in large-scale multimodal pre-training. In the context of the rapid growth of model size, it is necessary to seek efficient and flexible methods other than finetuning. In this paper, we propose to use prompt vectors to align the modalities. Our method achieves comparable performance to several other multimodal fusion methods in low-resource settings. We further show that our method is modular and parameter-efficient for processing tasks involving two or more data modalities.
BibTeX
@inproceedings{liang-etal-2022-modular,
title = "Modular and Parameter-Efficient Multimodal Fusion with Prompting",
author = "Liang, Sheng and
Zhao, Mengjie and
Schuetze, Hinrich",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-acl.234/",
doi = "10.18653/v1/2022.findings-acl.234",
pages = "2976--2985"
}