• Corpus ID: 239050439

Deep Image Matting with Flexible Guidance Input

@article{Cheng2021DeepIM,
  title={Deep Image Matting with Flexible Guidance Input},
  author={Hang Cheng and Shugong Xu and Xiufeng Jiang and Rongrong Wang},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.10898}
}
  • Hang Cheng, Shugong Xu, +1 author Rongrong Wang
  • Published 21 October 2021
  • Computer Science
  • ArXiv
Image matting is an important computer vision problem. Many existing matting methods require a hand-made trimap to provide auxiliary information, which is very expensive and limits the real world usage. Recently, some trimap-free methods have been proposed, which completely get rid of any user input. However, their performance lag far behind trimap-based methods due to the lack of guidance information. In this paper, we propose a matting method that use Flexible Guidance Input as user hint… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 21 REFERENCES
Natural Image Matting via Guided Contextual Attention
TLDR
This work develops a novel end-to-end approach for natural image matting with a guided contextual attention module, which is specifically designed for image matts and can mimic information flow of affinity-based methods and utilize rich features learned by deep neural networks simultaneously.
Deep Image Matting
TLDR
A novel deep learning based algorithm that can tackle image matting problems when an image has similar foreground and background colors or complicated textures and evaluation results demonstrate the superiority of this algorithm over previous methods.
Disentangled Image Matting
TLDR
This paper proposes AdaMatting, a new end-to-end matting framework that disentangles this problem into two sub-tasks: trimap adaptation and alpha estimation, which achieves the state-of-the-art performance on Adobe Composition-1k dataset both qualitatively and quantitatively.
Background Matting: The World Is Your Green Screen
TLDR
This work proposes a method for creating a matte – the per-pixel foreground color and alpha – of a person by taking photos or videos in an everyday setting with a handheld camera and trains a deep network with an adversarial loss to predict the matte.
Semantic Human Matting
TLDR
Semantic Human Matting (SHM) is the first algorithm that learns to jointly fit both semantic information and high quality details with deep networks and achieves comparable results with state-of-the-art interactive matting methods.
Context-Aware Image Matting for Simultaneous Foreground and Alpha Estimation
  • Qiqi Hou, F. Liu
  • Computer Science
    2019 IEEE/CVF International Conference on Computer Vision (ICCV)
  • 2019
TLDR
This paper presents a context-aware natural image matting method for simultaneous foreground and alpha matte estimation and employs two encoder networks to extract essential information for matting.
Attention-Guided Hierarchical Structure Aggregation for Image Matting
TLDR
An end-to-end Hierarchical Attention Matting Network (HAttMatting), which can predict the better structure of alpha mattes from single RGB images without additional input, and introduces a hybrid loss function fusing Structural SIMilarity, Mean Square Error and Adversarial loss to guide the network to further improve the overall foreground structure.
KNN Matting
TLDR
The matting technique, aptly called KNN matting, capitalizes on the nonlocal principle by using K nearest neighbors (KNN) in matching nonlocal neighborhoods, and contributes a simple and fast algorithm giving competitive results with sparse user markups.
Shared Sampling for Real‐Time Alpha Matting
TLDR
The first real‐time alpha matting technique for natural images and videos is presented, based on the observation that, for small neighborhoods, pixels tend to share similar attributes, and achieves speedups of up to two orders of magnitude compared to previous ones, while producing high‐quality alpha mattes.
A Closed-Form Solution to Natural Image Matting
TLDR
A closed-form solution to natural image matting that allows us to find the globally optimal alpha matte by solving a sparse linear system of equations and predicts the properties of the solution by analyzing the eigenvectors of a sparse matrix, closely related to matrices used in spectral image segmentation algorithms.
...
1
2
3
...