  Interactive segmentation enables users to extract masks by providing simple
annotations to indicate the target, such as boxes, clicks, or scribbles. Among
these interaction formats, scribbles are the most flexible as they can be of
arbitrary shapes and sizes. This enables scribbles to provide more indications
of the target object. However, previous works mainly focus on click-based
configuration, and the scribble-based setting is rarely explored. In this work,
we attempt to formulate a standard protocol for scribble-based interactive
segmentation. Basically, we design diversified strategies to simulate scribbles
for training, propose a deterministic scribble generator for evaluation, and
construct a challenging benchmark. Besides, we build a strong framework
ScribbleSeg, consisting of a Prototype Adaption Module(PAM) and a Corrective
Refine Module (CRM), for the task. Extensive experiments show that ScribbleSeg
performs notably better than previous click-based methods. We hope this could
serve as a more powerful and general solution for interactive segmentation. Our
code will be made available.
