Saliency Supervision: An Intuitive and Effective Approach for Pain Intensity Regression
This addresses the problem of pain intensity estimation for autonomous nursing systems, offering an intuitive approach that is incremental but effective for ill-posed datasets.
The paper tackles pain intensity regression from face images by introducing saliency supervision to regularize deep networks to focus on discriminative facial areas, achieving state-of-the-art performance on a widely-used dataset.
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.