Papers › MatteFormer: Transformer-Based Image Matting via Prior-Tokens
MatteFormer: Transformer-Based Image Matting via Prior-Tokens
Gyutae Park, Sungjoon Son, Jaeyoung Yoo, SeHo Kim, Nojun Kwak
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 https://github.com/webtoon/matteformer.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Matting | Composition-1K | MatteFormer | Conn | 18.9 | #5 of 13 | Archive leaderboard | report |
| Image Matting | Composition-1K | MatteFormer | Grad | 8.7 | #5 of 13 | Archive leaderboard | report |
| Image Matting | Composition-1K | MatteFormer | MSE | 4.0 | #5 of 13 | Archive leaderboard | report |
| Image Matting | Composition-1K | MatteFormer | SAD | 23.8 | #5 of 13 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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