  Although existing semantic segmentation approaches achieve impressive
results, they still struggle to update their models incrementally as new
categories are uncovered. Furthermore, pixel-by-pixel annotations are expensive
and time-consuming. This paper proposes a novel framework for Weakly
Incremental Learning for Semantic Segmentation, that aims at learning to
segment new classes from cheap and largely available image-level labels. As
opposed to existing approaches, that need to generate pseudo-labels offline, we
use an auxiliary classifier, trained with image-level labels and regularized by
the segmentation model, to obtain pseudo-supervision online and update the
model incrementally. We cope with the inherent noise in the process by using
soft-labels generated by the auxiliary classifier. We demonstrate the
effectiveness of our approach on the Pascal VOC and COCO datasets,
outperforming offline weakly-supervised methods and obtaining results
comparable with incremental learning methods with full supervision. Code can be
found at https://github.com/fcdl94/WILSON.
