Sparse Labeling Augmentation for Dense Models Training - Alonso, Íñigo; Murillo Arnal, Ana Cristina (Jornada de Jóvenes Investigadores del I3A, 2018)
This work proposes and validates a simple but effective approach to train dense semantic segmentation models from sparsely labeled data. Data and labeling collection is most costly task of semantic segmentation. Our approach needs only a few pixels per image reducing the human interaction required.