تقرير
Fully-attentive and interpretable: vision and video vision transformers for pain detection
العنوان: | Fully-attentive and interpretable: vision and video vision transformers for pain detection |
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المؤلفون: | Fiorentini, Giacomo, Ertugrul, Itir Onal, Salah, Albert Ali |
سنة النشر: | 2022 |
المجموعة: | Computer Science |
مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
الوصف: | Pain is a serious and costly issue globally, but to be treated, it must first be detected. Vision transformers are a top-performing architecture in computer vision, with little research on their use for pain detection. In this paper, we propose the first fully-attentive automated pain detection pipeline that achieves state-of-the-art performance on binary pain detection from facial expressions. The model is trained on the UNBC-McMaster dataset, after faces are 3D-registered and rotated to the canonical frontal view. In our experiments we identify important areas of the hyperparameter space and their interaction with vision and video vision transformers, obtaining 3 noteworthy models. We analyse the attention maps of one of our models, finding reasonable interpretations for its predictions. We also evaluate Mixup, an augmentation technique, and Sharpness-Aware Minimization, an optimizer, with no success. Our presented models, ViT-1 (F1 score 0.55 +- 0.15), ViViT-1 (F1 score 0.55 +- 0.13), and ViViT-2 (F1 score 0.49 +- 0.04), all outperform earlier works, showing the potential of vision transformers for pain detection. Code is available at https://github.com/IPDTFE/ViT-McMaster Comment: 9 pages (12 with references), 10 figures, VTTA2022 |
نوع الوثيقة: | Working Paper |
URL الوصول: | http://arxiv.org/abs/2210.15769 |
رقم الأكسشن: | edsarx.2210.15769 |
قاعدة البيانات: | arXiv |
الوصف غير متاح. |