Summary of Revisiting Multi-modal Emotion Learning with Broad State Space Models and Probability-guidance Fusion, by Yuntao Shou et al.
Revisiting Multi-modal Emotion Learning with Broad State Space Models and Probability-guidance Fusion
by Yuntao Shou, Tao Meng, Fuchen Zhang, Nan Yin, Keqin Li
First submitted to arxiv on: 27 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: None
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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 approach to multi-modal emotion recognition in conversations is introduced, which leverages State Space Models (SSMs) to improve performance. The method, called Broad Mamba, combines bidirectional SSM convolution for global context extraction and a probability-guided multi-modal fusion strategy. This allows the model to efficiently capture long-range dependencies without relying on self-attention mechanisms or sequence modeling. Experimental results demonstrate improved performance over previous methods, particularly when dealing with computationally expensive models like Transformers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to understand how people are feeling in a conversation. Most researchers try to mix together different types of information (like words and tone) to figure out the emotions. But what if we could look at the whole conversation instead of just parts of it? A new way to do this is by using something called State Space Models. This helps us understand how people’s emotions change over time. The team that created this method, called Broad Mamba, also came up with a special way to combine different types of information together. They tested their idea and found that it worked really well! |
Keywords
» Artificial intelligence » Multi modal » Probability » Self attention
