Summary of Synthetic Thermal and Rgb Videos For Automatic Pain Assessment Utilizing a Vision-mlp Architecture, by Stefanos Gkikas et al.
Synthetic Thermal and RGB Videos for Automatic Pain Assessment utilizing a Vision-MLP Architecture
by Stefanos Gkikas, Manolis Tsiknakis
First submitted to arxiv on: 29 Jul 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 The paper proposes an automatic pain assessment system that leverages Generative Adversarial Networks to generate synthetic thermal videos, which are then integrated into a pipeline for pain recognition. The authors develop a framework combining Vision-MLP and Transformer-based modules, using both RGB and synthetic thermal videos in unimodal and multimodal settings. Experimental results on facial video data from the BioVid database demonstrate the effectiveness of synthetic thermal videos and highlight their potential advantages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to assess pain in patients that uses computer-generated videos to help doctors better understand how much pain someone is experiencing. This system can be used to monitor patients’ pain levels over time, helping doctors make informed decisions about treatment. The researchers tested this approach using facial video data and found it was effective in recognizing pain. |
Keywords
» Artificial intelligence » Transformer
