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Modular and Parameter-Efficient Multimodal Fusion with Prompting

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arxiv 2203.08055 v1 pith:M2JMF52Z submitted 2022-03-15 cs.CL

Sheng Liang , Mengjie Zhao , Hinrich Schütze This is my paper
classification cs.CL
keywords multimodalfusionmethodmethodsmodalitiesmodularotherparameter-efficient
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. EPIC: Efficient Prompt Interaction for Text-Image Classification

    cs.CV 2025-07 conditional novelty 6.0 of 10

    EPIC uses similarity-gated cross-modal prompt interactions in frozen CLIP to improve text-image classification accuracy while training about 1% of parameters.

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