Matting Models¶
Deep Image Matting¶
Introduction¶
[ALGORITHM]
@inproceedings{xu2017deep,
title={Deep image matting},
author={Xu, Ning and Price, Brian and Cohen, Scott and Huang, Thomas},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={2970--2979},
year={2017}
}
Results and Models¶
| Method | SAD | MSE | GRAD | CONN | Download |
|---|---|---|---|---|---|
| stage1 (paper) | 54.6 | 0.017 | 36.7 | 55.3 | - |
| stage3 (paper) | 50.4 | 0.014 | 31.0 | 50.8 | - |
| stage1 (our) | 53.8 | 0.017 | 32.7 | 54.5 | model | log |
| stage2 (our) | 52.3 | 0.016 | 29.4 | 52.4 | model | log |
| stage3 (our) | 50.6 | 0.015 | 29.0 | 50.7 | model | log |
NOTE
stage1: train the encoder-decoder part without the refinement part. \
stage2: fix the encoder-decoder part and train the refinement part. \
stage3: fine-tune the whole network.
The performance of the model is not stable during the training. Thus, the reported performance is not from the last checkpoint. Instead, it is the best performance of all validations during training.
The performance of training (best performance) with different random seeds diverges in a large range. You may need to run several experiments for each setting to obtain the above performance.
Natural Image Matting via Guided Contextual Attention¶
Introduction¶
[ALGORITHM]
@inproceedings{li2020natural,
title={Natural Image Matting via Guided Contextual Attention},
author={Li, Yaoyi and Lu, Hongtao},
booktitle={Association for the Advancement of Artificial Intelligence (AAAI)},
year={2020}
}
Results and Models¶
| Method | SAD | MSE | GRAD | CONN | Download |
|---|---|---|---|---|---|
| baseline (paper) | 40.62 | 0.0106 | 21.53 | 38.43 | - |
| GCA (paper) | 35.28 | 0.0091 | 16.92 | 32.53 | - |
| baseline (our) | 36.50 | 0.0090 | 17.40 | 34.33 | model | log |
| GCA (our) | 34.77 | 0.0080 | 16.33 | 32.20 | model | log |
More results¶
| Method | SAD | MSE | GRAD | CONN | Download |
|---|---|---|---|---|---|
| baseline (with DIM pipeline) | 49.95 | 0.0144 | 30.21 | 49.67 | model | log |
| GCA (with DIM pipeline) | 49.42 | 0.0129 | 28.07 | 49.47 | model | log |
Indices Matter: Learning to Index for Deep Image Matting¶
Introduction¶
[ALGORITHM]
@inproceedings{hao2019indexnet,
title={Indices Matter: Learning to Index for Deep Image Matting},
author={Lu, Hao and Dai, Yutong and Shen, Chunhua and Xu, Songcen},
booktitle={Proc. IEEE/CVF International Conference on Computer Vision (ICCV)},
year={2019}
}
Results and Models¶
| Method | SAD | MSE | GRAD | CONN | Download |
|---|---|---|---|---|---|
| M2O DINs (paper) | 45.8 | 0.013 | 25.9 | 43.7 | - |
| M2O DINs (our) | 45.6 | 0.012 | 25.5 | 44.8 | model | log |
The performance of training (best performance) with different random seeds diverges in a large range. You may need to run several experiments for each setting to obtain the above performance.
More result¶
| Method | SAD | MSE | GRAD | CONN | Download |
|---|---|---|---|---|---|
| M2O DINs (with DIM pipeline) | 50.1 | 0.016 | 30.8 | 49.5 | model | log |