  Interactive segmentation enables users to segment as needed by providing cues
of objects, which introduces human-computer interaction for many fields, such
as image editing and medical image analysis. Typically, massive and expansive
pixel-level annotations are spent to train deep models by object-oriented
interactions with manually labeled object masks. In this work, we reveal that
informative interactions can be made by simulation with semantic-consistent yet
diverse region exploration in an unsupervised paradigm. Concretely, we
introduce a Multi-granularity Interaction Simulation (MIS) approach to open up
a promising direction for unsupervised interactive segmentation. Drawing on the
high-quality dense features produced by recent self-supervised models, we
propose to gradually merge patches or regions with similar features to form
more extensive regions and thus, every merged region serves as a
semantic-meaningful multi-granularity proposal. By randomly sampling these
proposals and simulating possible interactions based on them, we provide
meaningful interaction at multiple granularities to teach the model to
understand interactions. Our MIS significantly outperforms non-deep learning
unsupervised methods and is even comparable with some previous deep-supervised
methods without any annotation.
