  Traditional studies emphasize the significance of context information in
improving matting performance. Consequently, deep learning-based matting
methods delve into designing pooling or affinity-based context aggregation
modules to achieve superior results. However, these modules cannot well handle
the context scale shift caused by the difference in image size during training
and inference, resulting in matting performance degradation. In this paper, we
revisit the context aggregation mechanisms of matting networks and find that a
basic encoder-decoder network without any context aggregation modules can
actually learn more universal context aggregation, thereby achieving higher
matting performance compared to existing methods. Building on this insight, we
present AEMatter, a matting network that is straightforward yet very effective.
AEMatter adopts a Hybrid-Transformer backbone with appearance-enhanced
axis-wise learning (AEAL) blocks to build a basic network with strong context
aggregation learning capability. Furthermore, AEMatter leverages a large image
training strategy to assist the network in learning context aggregation from
data. Extensive experiments on five popular matting datasets demonstrate that
the proposed AEMatter outperforms state-of-the-art matting methods by a large
margin.
