Summary of Hique: Hierarchical Question Embedding Network For Multimodal Depression Detection, by Juho Jung et al.
HiQuE: Hierarchical Question Embedding Network for Multimodal Depression Detection
by Juho Jung, Chaewon Kang, Jeewoo Yoon, Seungbae Kim, Jinyoung Han
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multimedia (cs.MM)
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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 HiQuE framework is a novel depression detection system that leverages the hierarchical structure of clinical interview questions to capture the importance of each question in diagnosing depression. The model outperforms other state-of-the-art multimodal depression detection and emotion recognition models on the widely-used DAIC-WOZ dataset, showcasing its clinical utility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automated depression detection can help individuals receive early intervention for depression. This paper introduces a new system that looks at the order of questions asked during clinical interviews to better detect depression. The system, called HiQuE, does well compared to other models on a big dataset used in research. |
