Summary of Twins-painvit: Towards a Modality-agnostic Vision Transformer Framework For Multimodal Automatic Pain Assessment Using Facial Videos and Fnirs, by Stefanos Gkikas et al.
Twins-PainViT: Towards a Modality-Agnostic Vision Transformer Framework for Multimodal Automatic Pain Assessment using Facial Videos and fNIRS
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 This study proposes a multimodal framework for automatic pain assessment, which utilizes facial videos and functional near-infrared spectroscopy (fNIRS) to alleviate the need for domain-specific models. The framework employs a dual vision transformer (ViT) configuration and adopts waveform representations for fNIRS and extracted embeddings from both modalities. The proposed method achieves an accuracy of 46.76% in the multilevel pain assessment task, demonstrating its efficacy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps healthcare by developing a way to automatically assess pain. It uses two types of data: videos of people’s faces and information about brain activity. The new approach doesn’t need specific models for each type of data. The researchers tested their method and found it was good at guessing the level of pain someone is feeling. |
Keywords
» Artificial intelligence » Vision transformer » Vit
