  Manually labeling objects by tracing their boundaries is a laborious process.
In Polygon-RNN++ the authors proposed Polygon-RNN that produces polygonal
annotations in a recurrent manner using a CNN-RNN architecture, allowing
interactive correction via humans-in-the-loop. We propose a new framework that
alleviates the sequential nature of Polygon-RNN, by predicting all vertices
simultaneously using a Graph Convolutional Network (GCN). Our model is trained
end-to-end. It supports object annotation by either polygons or splines,
facilitating labeling efficiency for both line-based and curved objects. We
show that Curve-GCN outperforms all existing approaches in automatic mode,
including the powerful PSP-DeepLab and is significantly more efficient in
interactive mode than Polygon-RNN++. Our model runs at 29.3ms in automatic, and
2.6ms in interactive mode, making it 10x and 100x faster than Polygon-RNN++.
