Summary of Leveraging Retrieval Augment Approach For Multimodal Emotion Recognition Under Missing Modalities, by Qi Fan et al.
Leveraging Retrieval Augment Approach for Multimodal Emotion Recognition Under Missing Modalities
by Qi Fan, Hongyu Yuan, Haolin Zuo, Rui Liu, Guanglai Gao
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 This paper proposes a novel framework, Retrieval Augment for Missing Modality Multimodal Emotion Recognition (RAMER), to improve multimodal emotion recognition performance when modalities are missing. Traditional methods rely on internal reconstruction and multimodal joint learning, but this approach has limitations, especially when critical information is missing. RAMER leverages databases containing related multimodal emotion data to retrieve similar information, filling gaps left by missing modalities. Experimental results demonstrate the framework’s superiority over existing state-of-the-art approaches in missing modality MER tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multimodal emotion recognition is important, but what happens when some of the information is missing? For example, a video or audio recording might not be available because of technical issues. Traditional methods try to fix this by using other available data to fill in the gaps, but this approach has limitations. To address this challenge, researchers propose a new way to recognize emotions even when some of the information is missing. This method uses databases containing similar emotion-related data to help make predictions. The results show that this approach performs better than existing methods. |
