Faster, Better Blink Detection through Curriculum Learning by Augmentation
Ahmed Al‐Hindawi, Marcela Paola Vizcaychipi, Yiannis Demiris · 2022
Blinking is a useful biological signal that can gate gaze regression models to avoid the use of incorrect data in downstream tasks. Existing datasets are imbalanced both in frequency of class but also in intra-class difficulty which we demonstrate is a barrier for curriculum learning. We thus propose a novel curriculum augmentation scheme that aims to address frequency and difficulty imbalances implicitly which are are terming Curriculum Learning by Augmentation (CLbA).