抠图... ... At the core of our approach is a deep matting netw

算法

Background-Matting：The World is Your Green Screen（2020）

历史

2019
- Disentangled image matting
- Context-aware image matting for simultaneous foreground and alpha estimation
- Learning to index for deep image matting
- A late fusion cnn for digital matting
2018
- Semantic soft segmentation
- Encoder-decoder with atrous separable convlution for semantic image segmentation
- Semantic human matting
- Alpha-gan: Generative adversarial networks for natural image matting
2017
- Designing effective inter-pixel information flow for natural image matting
- Deep image matting
- Fast deep matting for portrait animation on mobile phone
2016
- Natural image matting using deep convolutional neural networks
- Deep automatic portrait matting
2013
- KNN matting
2011
- A global sampling method for alpha matting
- Nonlocal matting
2010
- Shared sampling for real-time alpha matting
- Fast matting using large kernel matting laplacian matrics
2008
- Spectral matting
2007
- A closed-form solution to natural image matting
2004
- A bayesian approach to digital matting

网络结构

At the core of our approach is a deep matting network G that extracts foreground color and alpha for a given input frame, augmented with background, soft segmentation, and (optionally nearby video frames), and a discriminator network D that guides the training to generate realistic results.

损失函数

Attention-Guided Hierarchical Structure Aggregation for Image Matting（2020）

[CVPR2020-HAttMatting] [Attention-Guided Hierarchical Structure Aggregation for Image Matting]

A Late Fusion CNN for Digital Matting(2019)

[FusionMatting]

[A Late Fusion CNN for Digital Matting]

[《A Late Fusion CNN for Digital Matting》论文阅读]

[[质疑][CVPR2019][A Late Fusion... Matting]]

[澄清误解-对CVPR 2019 LFM论文质疑的回复]

[阿里巴巴-浙江大学前言技术联合研究中心]

LFM是端到端的神经网络，输入包含前景的图像，输出为前景的alpha遮罩。利用神经网络来预测三个图：前景概率图、背景概率图和混合权重图。根据混合权重图将前景概率图和背景概率图进行融合得到alpha遮罩。需要训练的网络有分割网络预训练、融合网络预训练以及端到端的联合训练，训练损失加在输出alpha遮罩上。