  Over the last few years, deep learning based approaches have achieved
outstanding improvements in natural image matting. Many of these methods can
generate visually plausible alpha estimations, but typically yield blurry
structures or textures in the semitransparent area. This is due to the local
ambiguity of transparent objects. One possible solution is to leverage the
far-surrounding information to estimate the local opacity. Traditional
affinity-based methods often suffer from the high computational complexity,
which are not suitable for high resolution alpha estimation. Inspired by
affinity-based method and the successes of contextual attention in inpainting,
we develop a novel end-to-end approach for natural image matting with a guided
contextual attention module, which is specifically designed for image matting.
Guided contextual attention module directly propagates high-level opacity
information globally based on the learned low-level affinity. The proposed
method can mimic information flow of affinity-based methods and utilize rich
features learned by deep neural networks simultaneously. Experiment results on
Composition-1k testing set and alphamatting.com benchmark dataset demonstrate
that our method outperforms state-of-the-art approaches in natural image
matting. Code and models are available at
https://github.com/Yaoyi-Li/GCA-Matting.
