Summary of Enhancing Multimodal Sentiment Analysis For Missing Modality Through Self-distillation and Unified Modality Cross-attention, by Yuzhe Weng et al.
Enhancing Multimodal Sentiment Analysis for Missing Modality through Self-Distillation and Unified Modality Cross-Attention
by Yuzhe Weng, Haotian Wang, Tian Gao, Kewei Li, Shutong Niu, Jun Du
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 study develops a robust model for multimodal sentiment analysis that effectively integrates information from text, audio, and video modalities. The Double-Flow Self-Distillation Framework, including Unified Modality Cross-Attention (UMCA) and Modality Imagination Autoencoder (MIA), excels at processing scenarios with complete or missing modalities. When the text modality is absent, the framework uses a Large Language Model (LLM)-based model to simulate the text representation from audio, while MIA supplements information from other modalities. The study also introduces Rank-N Contrast (RNC) loss function to align simulated and real representations and capture continuous nature of sentiment valence regression tasks. The model achieves outstanding performance on MAE and outperforms others when the text modality is missing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research develops a new way to analyze how people feel about things they say, hear, or see. It’s hard to get good data for this kind of analysis because it’s expensive to label the text part and computer-generated speech can be bad quality. The scientists created a special model that uses information from all three parts (text, audio, and video) even when one part is missing. They also came up with a new way to measure how well their model does. The results show that their model is really good at predicting people’s feelings and better than other models when the text part is not available. |
Keywords
» Artificial intelligence » Autoencoder » Cross attention » Distillation » Large language model » Loss function » Mae » Regression
