Transformer with Leveraged Masked Autoencoder for video-based Pain Assessment
This addresses pain recognition for populations unable to communicate, such as in healthcare, but appears incremental as it combines existing methods.
The paper tackled pain assessment from facial video data by enhancing a Transformer-based model with a Masked Autoencoder to capture expressions and micro-expressions, achieving promising results on the AI4Pain dataset.
Accurate pain assessment is crucial in healthcare for effective diagnosis and treatment; however, traditional methods relying on self-reporting are inadequate for populations unable to communicate their pain. Cutting-edge AI is promising for supporting clinicians in pain recognition using facial video data. In this paper, we enhance pain recognition by employing facial video analysis within a Transformer-based deep learning model. By combining a powerful Masked Autoencoder with a Transformers-based classifier, our model effectively captures pain level indicators through both expressions and micro-expressions. We conducted our experiment on the AI4Pain dataset, which produced promising results that pave the way for innovative healthcare solutions that are both comprehensive and objective.