In this paper, we propose a transformer-based image matting model called
MatteFormer, which takes full advantage of trimap information in the
transformer block. Our method first introduces a prior-token which is a global
representation of each trimap region (e.g. foreground, background and unknown).
These prior-tokens are used as global priors and participate in the
self-attention mechanism of each block. Each stage of the encoder is composed
of PAST (Prior-Attentive Swin Transformer) block, which is based on the Swin
Transformer block, but differs in a couple of aspects: 1) It has PA-WSA
(Prior-Attentive Window Self-Attention) layer, performing self-attention not
only with spatial-tokens but also with prior-tokens. 2) It has prior-memory
which saves prior-tokens accumulatively from the previous blocks and transfers
them to the next block. We evaluate our MatteFormer on the commonly used image
matting datasets: Composition-1k and Distinctions-646. Experiment results show
that our proposed method achieves state-of-the-art performance with a large
margin. Our codes are available at this https URL

本文提出了一种基于 transformer 的图像抠图模型 MatteFormer，利用 trimap 信息；方法采用优先标记，并在每个块的自注意机制中使用全局先验，评估结果表明其可以在基准数据集上实现最领先的性能表现。