  Interactive segmentation entails a human marking an image to guide how a
model either creates or edits a segmentation. Our work addresses limitations of
existing methods: they either only support one gesture type for marking an
image (e.g., either clicks or scribbles) or require knowledge of the gesture
type being employed, and require specifying whether marked regions should be
included versus excluded in the final segmentation. We instead propose a
simplified interactive segmentation task where a user only must mark an image,
where the input can be of any gesture type without specifying the gesture type.
We support this new task by introducing the first interactive segmentation
dataset with multiple gesture types as well as a new evaluation metric capable
of holistically evaluating interactive segmentation algorithms. We then analyze
numerous interactive segmentation algorithms, including ones adapted for our
novel task. While we observe promising performance overall, we also highlight
areas for future improvement. To facilitate further extensions of this work, we
publicly share our new dataset at https://github.com/joshmyersdean/dig.
