  Segmenting objects of interest in an image is an essential building block of
applications such as photo-editing and image analysis. Under interactive
settings, one should achieve good segmentations while minimizing user input.
Current deep learning-based interactive segmentation approaches use early
fusion and incorporate user cues at the image input layer. Since segmentation
CNNs have many layers, early fusion may weaken the influence of user
interactions on the final prediction results. As such, we propose a new
multi-stage guidance framework for interactive segmentation. By incorporating
user cues at different stages of the network, we allow user interactions to
impact the final segmentation output in a more direct way. Our proposed
framework has a negligible increase in parameter count compared to early-fusion
frameworks. We perform extensive experimentation on the standard interactive
instance segmentation and one-click segmentation benchmarks and report
state-of-the-art performance.
