  Manually labeling datasets with object masks is extremely time consuming. In
this work, we follow the idea of Polygon-RNN to produce polygonal annotations
of objects interactively using humans-in-the-loop. We introduce several
important improvements to the model: 1) we design a new CNN encoder
architecture, 2) show how to effectively train the model with Reinforcement
Learning, and 3) significantly increase the output resolution using a Graph
Neural Network, allowing the model to accurately annotate high-resolution
objects in images. Extensive evaluation on the Cityscapes dataset shows that
our model, which we refer to as Polygon-RNN++, significantly outperforms the
original model in both automatic (10% absolute and 16% relative improvement in
mean IoU) and interactive modes (requiring 50% fewer clicks by annotators). We
further analyze the cross-domain scenario in which our model is trained on one
dataset, and used out of the box on datasets from varying domains. The results
show that Polygon-RNN++ exhibits powerful generalization capabilities,
achieving significant improvements over existing pixel-wise methods. Using
simple online fine-tuning we further achieve a high reduction in annotation
time for new datasets, moving a step closer towards an interactive annotation
tool to be used in practice.
