Interactive object cutout tools are the cornerstone of the image editing
workflow. Recent deep-learning based interactive segmentation algorithms have
made significant progress in handling complex images and rough binary
selections can typically be obtained with just a few clicks. Yet, deep learning
techniques tend to plateau once this rough selection has been reached. In this
work, we interpret this plateau as the inability of current algorithms to
sufficiently leverage each user interaction and also as the limitations of
current training/testing datasets.
We propose a novel interactive architecture and a novel training scheme that
are both tailored to better exploit the user workflow. We also show that
significant improvements can be further gained by introducing a synthetic
training dataset that is specifically designed for complex object boundaries.
Comprehensive experiments support our approach, and our network achieves state
of the art performance.

本文提出了一种新的交互式架构和训练方案，旨在更好地利用用户工作流，并展示出引入专门设计用于复杂对象边界的合成训练数据集可以进一步获得显着的改进，该网络达到了最先进性能。