Image matting is a long-standing problem in computer graphics and vision,
mostly identified as the accurate estimation of the foreground in input images.
We argue that the foreground objects can be represented by different-level
information, including the central bodies, large-grained boundaries, refined
details, etc. Based on this observation, in this paper, we propose a
multi-scale information assembly framework (MSIA-matte) to pull out
high-quality alpha mattes from single RGB images. Technically speaking, given
an input image, we extract advanced semantics as our subject content and retain
initial CNN features to encode different-level foreground expression, then
combine them by our well-designed information assembly strategy. Extensive
experiments can prove the effectiveness of the proposed MSIA-matte, and we can
achieve state-of-the-art performance compared to most existing matting
networks.

本文提出了一个名为 MSIA-matte 的多尺度信息组装框架，旨在从单个 RGB 图像中提取高质量的 alpha mattes。通过提取高级语义作为主题内容和保留初始 CNN 特征来编码不同级别的前景表达，然后通过设计良好的信息组装策略将它们组合在一起。实验表明，所提出的方法可以达到最先进的性能水平，与大多数现有的抠除网络相比，取得了很好的效果。