Core Concepts
MoPE enhances multimodal fusion by introducing prompt experts for adaptive and scalable performance.
Abstract
In this article, the authors introduce the MoPE technique to improve multimodal fusion. They disentangle prompts for adaptiveness and scalability, achieving state-of-the-art results with parameter efficiency. The method is compared to existing techniques across various datasets, showcasing its effectiveness.
Directory:
- Introduction to Prompt Tuning (Abstract)
- Prompt-tuning demonstrated parameter-efficiency in fusing unimodal models.
- Challenges in Multimodal Fusion
- Limited adaptivity and expressiveness of vanilla prompting.
- Proposed Solution: MoPE Technique
- Disentangles prompts for adaptive capture of features.
- Introduces multiple prompt experts for enhanced expressiveness.
- Experimental Results
- Achieved state-of-the-art results on three multimodal datasets.
- Comparison with Existing Methods
- Outperformed previous prompt-based fusion methods by a significant margin.
- Analysis and Discussion
- Explored factors contributing to the method's increased expressiveness and limitations.
Stats
"requiring only 0.8% of the trainable parameters."
"state-of-the-art results"
"extensive experiments across three multimodal datasets"
"parameter-scaling tends to be a better alternative than length-scaling"