Summary of Knowledge-guided Dynamic Modality Attention Fusion Framework For Multimodal Sentiment Analysis, by Xinyu Feng et al.
Knowledge-Guided Dynamic Modality Attention Fusion Framework for Multimodal Sentiment Analysis
by Xinyu Feng, Yuming Lin, Lihua He, You Li, Liang Chang, Ya Zhou
First submitted to arxiv on: 6 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Knowledge-Guided Dynamic Modality Attention Fusion Framework (KuDA) for multimodal sentiment analysis addresses the limitation of previous methods, which neglect the situation where each modality may become dominant. KuDA uses sentiment knowledge to guide the model in dynamically selecting the dominant modality and adjusting the contributions of each modality. The framework is evaluated on four benchmark datasets, achieving state-of-the-art performance and adaptability to different scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to analyze how people feel about something using data from multiple sources, like text, images, or audio. Current methods treat all these sources equally, which doesn’t always work well. KuDA is a better approach that takes into account the situation where one source might be more important than others. It uses knowledge about sentiment to decide which source is most important and adjust its contribution accordingly. |
Keywords
» Artificial intelligence » Attention
