补全模型¶
AOT-GAN (TVCG’2021)¶
摘要¶
结果与模型¶
Places365-Challenge
算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | 下载 |
---|---|---|---|---|---|---|---|---|
AOT-GAN | free-form (50-60%) | 512x512 | 500k | Places365-val | 7.07 | 19.01 | 0.682 | 模型 | 日志 |
评估指标 | 掩膜缺损 | 论文结果 | 复现结果 |
---|---|---|---|
L1 (10^-2) | 1 – 10% | 0.55 | 0.54 |
(lower better) | 10 – 20% | 1.19 | 1.47 |
20 – 30% | 2.11 | 2.79 | |
30 – 40% | 3.20 | 4.38 | |
40 – 50% | 4.51 | 6.28 | |
50 – 60% | 7.07 | 10.16 | |
PSNR | 1 – 10% | 34.79 | inf |
(higher better) | 10 – 20% | 29.49 | 31.22 |
20 – 30% | 26.03 | 27.65 | |
30 – 40% | 23.58 | 25.06 | |
40 – 50% | 21.65 | 23.01 | |
50 – 60% | 19.01 | 20.05 | |
SSIM | 1 – 10% | 0.976 | 0.982 |
(higher better) | 10 – 20% | 0.940 | 0.951 |
20 – 30% | 0.890 | 0.911 | |
30 – 40% | 0.835 | 0.866 | |
40 – 50% | 0.773 | 0.815 | |
50 – 60% | 0.682 | 0.739 |
引用¶
@inproceedings{yan2021agg,
author = {Zeng, Yanhong and Fu, Jianlong and Chao, Hongyang and Guo, Baining},
title = {Aggregated Contextual Transformations for High-Resolution Image Inpainting},
booktitle = {Arxiv},
pages={-},
year = {2020}
}
DeepFillv1 (CVPR’2018)¶
DeepFillv1 (CVPR'2018)
@inproceedings{yu2018generative,
title={Generative image inpainting with contextual attention},
author={Yu, Jiahui and Lin, Zhe and Yang, Jimei and Shen, Xiaohui and Lu, Xin and Huang, Thomas S},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={5505--5514},
year={2018}
}
Places365-Challenge
算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | 下载 |
---|---|---|---|---|---|---|---|---|
DeepFillv1 | square bbox | 256x256 | 3500k | Places365-val | 11.019 | 23.429 | 0.862 | 模型 | 日志 |
CelebA-HQ
算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | 下载 |
---|---|---|---|---|---|---|---|---|
DeepFillv1 | square bbox | 256x256 | 1500k | CelebA-val | 6.677 | 26.878 | 0.911 | 模型 | 日志 |
DeepFillv2 (CVPR’2019)¶
DeepFillv2 (CVPR'2019)
@inproceedings{yu2019free,
title={Free-form image inpainting with gated convolution},
author={Yu, Jiahui and Lin, Zhe and Yang, Jimei and Shen, Xiaohui and Lu, Xin and Huang, Thomas S},
booktitle={Proceedings of the IEEE International Conference on Computer Vision},
pages={4471--4480},
year={2019}
}
Places365-Challenge
算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | 下载 |
---|---|---|---|---|---|---|---|---|
DeepFillv2 | free-form | 256x256 | 100k | Places365-val | 8.635 | 22.398 | 0.815 | 模型 | 日志 |
CelebA-HQ
算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | 下载 |
---|---|---|---|---|---|---|---|---|
DeepFillv2 | free-form | 256x256 | 20k | CelebA-val | 5.411 | 25.721 | 0.871 | 模型 | 日志 |
Global&Local (ToG’2017)¶
Global&Local (ToG'2017)
@article{iizuka2017globally,
title={Globally and locally consistent image completion},
author={Iizuka, Satoshi and Simo-Serra, Edgar and Ishikawa, Hiroshi},
journal={ACM Transactions on Graphics (ToG)},
volume={36},
number={4},
pages={1--14},
year={2017},
publisher={ACM New York, NY, USA}
}
请注意,为了与当前的深度图像修复方法进行公平比较,我们没有在 Global&Local 中使用后处理模块。
Places365-Challenge
算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | 下载 |
---|---|---|---|---|---|---|---|---|
Global&Local | square bbox | 256x256 | 500k | Places365-val | 11.164 | 23.152 | 0.862 | 模型 | 日志 |
CelebA-HQ
算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | 下载 |
---|---|---|---|---|---|---|---|---|
Global&Local | square bbox | 256x256 | 500k | CelebA-val | 6.678 | 26.780 | 0.904 | 模型 | 日志 |
PConv (ECCV’2018)¶
PConv (ECCV'2018)
@inproceedings{liu2018image,
title={Image inpainting for irregular holes using partial convolutions},
author={Liu, Guilin and Reda, Fitsum A and Shih, Kevin J and Wang, Ting-Chun and Tao, Andrew and Catanzaro, Bryan},
booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
pages={85--100},
year={2018}
}
Places365-Challenge
算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | 下载 |
---|---|---|---|---|---|---|---|---|
PConv | free-form | 256x256 | 500k | Places365-val | 8.776 | 22.762 | 0.801 | 模型 | 日志 |
CelebA-HQ
算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | 下载 |
---|---|---|---|---|---|---|---|---|
PConv | free-form | 256x256 | 500k | CelebA-val | 5.990 | 25.404 | 0.853 | 模型 | 日志 |