Summary of Cormult: a Semi-supervised Modality Correlation-aware Multimodal Transformer For Sentiment Analysis, by Yangmin Li et al.
CorMulT: A Semi-supervised Modality Correlation-aware Multimodal Transformer for Sentiment Analysis
by Yangmin Li, Ruiqi Zhu, Wengen Li
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 A novel multimodal sentiment analysis approach is proposed, leveraging correlations between text, image, and audio modalities to improve emotion analysis. The authors classify existing methods into modality interaction-based, transformation-based, and similarity-based approaches, highlighting their limitations in handling weak correlations between modalities. To address this issue, the Correlation-aware Multimodal Transformer (CorMulT) is developed, consisting of a pre-training stage that learns modality correlation coefficients using contrastive learning and a prediction stage that fuses these coefficients with modality representations for sentiment prediction. Experiments on the CMU-MOSEI dataset demonstrate CorMulT’s superiority over state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multimodal sentiment analysis is important because it helps machines understand human emotions. Researchers have developed different ways to do this, but most rely on strong connections between different types of data (like text and images). These methods don’t work well when the connections are weak. To fix this problem, a new approach called CorMulT was created. It has two stages: one that learns how different types of data relate to each other, and another that uses this information to predict emotions. The results show that CorMulT is better than existing methods at understanding human emotions. |
Keywords
» Artificial intelligence » Transformer
