  In interactive instance segmentation, users give feedback to iteratively
refine segmentation masks. The user-provided clicks are transformed into
guidance maps which provide the network with necessary cues on the whereabouts
of the object of interest. Guidance maps used in current systems are purely
distance-based and are either too localized or non-informative. We propose a
novel transformation of user clicks to generate scale-aware guidance maps that
leverage the hierarchical structural information present in an image. Using our
guidance maps, even the most basic FCNs are able to outperform existing
approaches that require state-of-the-art segmentation networks pre-trained on
large scale segmentation datasets. We demonstrate the effectiveness of our
proposed transformation strategy through comprehensive experimentation in which
we significantly raise state-of-the-art on four standard interactive
segmentation benchmarks.
