  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
