  Deep image matting methods have achieved increasingly better results on
benchmarks (e.g., Composition-1k/alphamatting.com). However, the robustness,
including robustness to trimaps and generalization to images from different
domains, is still under-explored. Although some works propose to either refine
the trimaps or adapt the algorithms to real-world images via extra data
augmentation, none of them has taken both into consideration, not to mention
the significant performance deterioration on benchmarks while using those data
augmentation. To fill this gap, we propose an image matting method which
achieves higher robustness (RMat) via multilevel context assembling and strong
data augmentation targeting matting. Specifically, we first build a strong
matting framework by modeling ample global information with transformer blocks
in the encoder, and focusing on details in combination with convolution layers
as well as a low-level feature assembling attention block in the decoder. Then,
based on this strong baseline, we analyze current data augmentation and explore
simple but effective strong data augmentation to boost the baseline model and
contribute a more generalizable matting method. Compared with previous methods,
the proposed method not only achieves state-of-the-art results on the
Composition-1k benchmark (11% improvement on SAD and 27% improvement on Grad)
with smaller model size, but also shows more robust generalization results on
other benchmarks, on real-world images, and also on varying coarse-to-fine
trimaps with our extensive experiments.
