Summary of Enriching Multimodal Sentiment Analysis Through Textual Emotional Descriptions Of Visual-audio Content, by Sheng Wu et al.
Enriching Multimodal Sentiment Analysis through Textual Emotional Descriptions of Visual-Audio Content
by Sheng Wu, Xiaobao Wang, Longbiao Wang, Dongxiao He, Jianwu Dang
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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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 framework, DEVA, aims to improve multimodal sentiment analysis by incorporating textual sentiment descriptions into the fusion process. The model uses an Emotional Description Generator (EDG) to convert raw audio and visual data into textualized sentiment descriptions, enhancing their emotional characteristics. DEVA also employs a Text-guided Progressive Fusion Module (TPF), which leverages varying levels of text as a core modality guide to alleviate disparities between text and visual-audio modalities. Experimental results on widely used sentiment analysis benchmark datasets demonstrate significant enhancements compared to state-of-the-art models, with robust sensitivity to subtle emotional variations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DEVA is a new way to understand how people feel based on what they say and show through audio and video. It helps computers recognize emotions by combining words, sounds, and pictures into one meaningful description. This makes it better at understanding emotions than other approaches that only use one type of data. The creators tested DEVA with many different kinds of audio and video clips and found that it did a great job of identifying subtle emotional changes. |
