  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.
