Summary of A Full Transformer-based Framework For Automatic Pain Estimation Using Videos, by Stefanos Gkikas et al.
A Full Transformer-based Framework for Automatic Pain Estimation using Videos
by Stefanos Gkikas, Manolis Tsiknakis
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 presents a novel full transformer-based framework for automatic pain estimation in designing an optimal pain management system. The proposed Transformer in Transformer (TNT) model combines cross-attention and self-attention blocks to achieve state-of-the-art performances on video-based pain estimation tasks from the BioVid database. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how a machine learning approach can help reduce patient suffering by providing reliable pain assessment. By using videos from the BioVid database, the researchers demonstrated that their method is not only effective but also efficient and generalizable across different pain estimation tasks. |
Keywords
» Artificial intelligence » Cross attention » Machine learning » Self attention » Transformer
