  Getting pain intensity from face images is an important problem in autonomous
nursing systems. However, due to the limitation in data sources and the
subjectiveness in pain intensity values, it is hard to adopt modern deep neural
networks for this problem without domain-specific auxiliary design. Inspired by
human vision priori, we propose a novel approach called saliency supervision,
where we directly regularize deep networks to focus on facial area that is
discriminative for pain regression. Through alternative training between
saliency supervision and global loss, our method can learn sparse and robust
features, which is proved helpful for pain intensity regression. We verified
saliency supervision with face-verification network backbone on the widely-used
dataset, and achieved state-of-art performance without bells and whistles. Our
saliency supervision is intuitive in spirit, yet effective in performance. We
believe such saliency supervision is essential in dealing with ill-posed
datasets, and has potential in a wide range of vision tasks.
