Summary of Lmvd: a Large-scale Multimodal Vlog Dataset For Depression Detection in the Wild, by Lang He et al.
LMVD: A Large-Scale Multimodal Vlog Dataset for Depression Detection in the Wild
by Lang He, Kai Chen, Junnan Zhao, Yimeng Wang, Ercheng Pei, Haifeng Chen, Jiewei Jiang, Shiqing Zhang, Jie Zhang, Zhongmin Wang, Tao He, Prayag Tiwari
First submitted to arxiv on: 9 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 paper proposes a novel deep learning architecture, MDDformer, for detecting depression in individuals. The model is trained on a large-scale multimodal vlog dataset (LMVD) containing over 1823 samples with 214 hours of footage from four multimedia platforms. The LMVD dataset addresses the lack of publicly available data for depression detection due to privacy concerns. The paper demonstrates the effectiveness of MDDformer through extensive validations on the LMVD dataset, achieving superior performance in detecting depression. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new AI model helps detect depression by analyzing people’s behavior online. Researchers created a large database of videos from social media and YouTube to train the model. This can help doctors better diagnose depression and develop more effective treatments. The data will be shared publicly so other scientists can use it too. |
Keywords
» Artificial intelligence » Deep learning
