  Recently, plain vision Transformers (ViTs) have shown impressive performance
on various computer vision tasks, thanks to their strong modeling capacity and
large-scale pretraining. However, they have not yet conquered the problem of
image matting. We hypothesize that image matting could also be boosted by ViTs
and present a new efficient and robust ViT-based matting system, named
ViTMatte. Our method utilizes (i) a hybrid attention mechanism combined with a
convolution neck to help ViTs achieve an excellent performance-computation
trade-off in matting tasks. (ii) Additionally, we introduce the detail capture
module, which just consists of simple lightweight convolutions to complement
the detailed information required by matting. To the best of our knowledge,
ViTMatte is the first work to unleash the potential of ViT on image matting
with concise adaptation. It inherits many superior properties from ViT to
matting, including various pretraining strategies, concise architecture design,
and flexible inference strategies. We evaluate ViTMatte on Composition-1k and
Distinctions-646, the most commonly used benchmark for image matting, our
method achieves state-of-the-art performance and outperforms prior matting
works by a large margin.
