  Natural image matting aims to estimate the alpha matte of the foreground from
a given image. Various approaches have been explored to address this problem,
such as interactive matting methods that use guidance such as click or trimap,
and automatic matting methods tailored to specific objects. However, existing
matting methods are designed for specific objects or guidance, neglecting the
common requirement of aggregating global and local contexts in image matting.
As a result, these methods often encounter challenges in accurately identifying
the foreground and generating precise boundaries, which limits their
effectiveness in unforeseen scenarios. In this paper, we propose a simple and
universal matting framework, named Dual-Context Aggregation Matting (DCAM),
which enables robust image matting with arbitrary guidance or without guidance.
Specifically, DCAM first adopts a semantic backbone network to extract
low-level features and context features from the input image and guidance.
Then, we introduce a dual-context aggregation network that incorporates global
object aggregators and local appearance aggregators to iteratively refine the
extracted context features. By performing both global contour segmentation and
local boundary refinement, DCAM exhibits robustness to diverse types of
guidance and objects. Finally, we adopt a matting decoder network to fuse the
low-level features and the refined context features for alpha matte estimation.
Experimental results on five matting datasets demonstrate that the proposed
DCAM outperforms state-of-the-art matting methods in both automatic matting and
interactive matting tasks, which highlights the strong universality and high
performance of DCAM. The source code is available at
\url{https://github.com/Windaway/DCAM}.
