  Dense pixel-wise classification maps output by deep neural networks are of
extreme importance for scene understanding. However, these maps are often
partially inaccurate due to a variety of possible factors. Therefore, we
propose to interactively refine them within a framework named DISCA (Deep Image
Segmentation with Continual Adaptation). It consists of continually adapting a
neural network to a target image using an interactive learning process with
sparse user annotations as ground-truth. We show through experiments on three
datasets using synthesized annotations the benefits of the approach, reaching
an IoU improvement up to 4.7% for ten sampled clicks. Finally, we exhibit that
our approach can be particularly rewarding when it is faced to additional
issues such as domain adaptation.
