  Annotating object boundaries by humans demands high costs. Recently,
polygon-based annotation methods with human interaction have shown successful
performance. However, given the connected vertex topology, these methods
exhibit difficulty predicting the disconnected components in an object. This
paper introduces Split-GCN, a novel architecture based on the polygon approach
and self-attention mechanism. By offering the direction information, Split-GCN
enables the polygon vertices to move more precisely to the object boundary. Our
model successfully predicts disconnected components of an object by
transforming the initial topology using the context exchange about the
dependencies of vertices. Split-GCN demonstrates competitive performance with
the state-of-the-art models on Cityscapes and even higher performance with the
baseline models. On four cross-domain datasets, we confirm our model's
generalization ability.
