Modular and Parameter-Efficient Multimodal Fusion with Prompting

Liang, Sheng; Zhao, Mengjie and Schütze, Hinrich (May 2022): Modular and Parameter-Efficient Multimodal Fusion with Prompting. ACL 2022, Dublin, Ireland, May 2022. Muresan, Smaranda; N, Preslav and Villavicencio, Aline (eds.) : In: Findings of the Association for Computational Linguistics: ACL 2022, Stroudsburg, PA: Association for Computational Linguistics. 2976 -2985

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.

• Item Type: Conference or Workshop Item (Paper)
• EU Funded Grant Agreement Number: 740516
• EU Projects: Horizon 2020 > ERC Grants > ERC Advanced Grant > ERC Grant 740516: NonSequeToR - Non-sequence models for tokenization replacement
• Research Centers: Center for Information and Language Processing (CIS)
• Subjects: 000 Computer science, information and general works > 000 Computer science, knowledge, and systems; 400 Language > 400 Language; 400 Language > 410 Linguistics
• Place of Publication: Stroudsburg, PA
• Language: English