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MatteFormer

This repository includes the official project of MatteFormer, presented in our paper: MatteFormer: Transformer-Based Image Matting via Prior-Tokens [CVPR 22]

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.

Requirements

The codes are tested in the following environment:

• python 3.8
• pytorch 1.10.1
• CUDA 10.2 & CuDNN 8

Performances

Data Preparation

1] Get DIM dataset on Deep Image Matting.

2] For DIM dataset preparation, please refer to GCA-Matting.

• For Training, merge 'Adobe-licensed images' and 'Other' folder to use all 431 foregrounds and alphas
• For Testing, use 'Composition_code.py' and 'copy_testing_alpha.sh' in GCA-Matting.

3] For background images, Download dataset on PASCAL and COCO.

*If you want to download prepared test set directly : download link

Testing on Composition-1k dataset

pip3 install -r requirements.txt

1] Run inference code (the predicted alpha will be save to ./predDIM/pred_alpha by default)

python3 infer.py

2] Evaluate the results by the official evaluation MATLAB code ./DIM_evaluation_code/evaluate.m (provided by Deep Image Matting)

3] You can also check out the evaluation result simplicity with the python code (un-official)

python3 evaluate.py

Training on Composition-1k dataset

1] You can get (imagenet pretrained) swin-transformer tiny model ('swin_tiny_patch4_window7_224.pth') on Swin Transformer.

2] modify "config/MatteFormer_Composition1k.toml"

3] run main.py

CUDA_VISIBLE_DEVICES=0,1 python3 -m torch.distributed.launch --nproc_per_node=2 main.py

Citation

If you find this work or code useful for your research, please use the following BibTex entry:

@article{park2022matteformer,
  title={MatteFormer: Transformer-Based Image Matting via Prior-Tokens},
  author={Park, GyuTae and Son, SungJoon and Yoo, JaeYoung and Kim, SeHo and Kwak, Nojun},
  journal={arXiv preprint arXiv:2203.15662},
  year={2022}
}

Acknowledgment

• Our Codes are mainly originated from MG-Matting
• Also, we build our codes with reference as GCA-Matting and Swin Transformer for Semantic Segmentation

License

MatteFormer is licensed under Apache-2.0, except utils/logger.py which is adopted from https://github.com/JiahuiYu/generative_inpainting under CC BY-NC 4.0. See LICENSE for the full license text.

MatteFormer

Copyright 2022-present NAVER WEBTOON

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

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