# Highly Efficient Natural Image Matting

arXiv: https://arxiv.org/abs/2110.12748 · HTML: https://mlsanity.com/paper/2110.12748/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 55 references that are in the map.
- First posted on arXiv 25 Oct 2021.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://mlsanity.com/paper/1812.01187/) (2018)
- [Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network](https://mlsanity.com/paper/1703.02719/) (2017)
- [Real-Time High-Resolution Background Matting](https://mlsanity.com/paper/2012.07810/) (2020)
- [Deep Image Matting](https://mlsanity.com/paper/1703.03872/) (2017)
- [Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution](https://mlsanity.com/paper/1904.05049/) (2019)
- [Background Matting: The World is Your Green Screen](https://mlsanity.com/paper/2004.00626/) (2020)
- [Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation](https://mlsanity.com/paper/1904.06807/) (2019)
- [Indices Matter: Learning to Index for Deep Image Matting](https://mlsanity.com/paper/1908.00672/) (2019)
- [XingGAN for Person Image Generation](https://mlsanity.com/paper/2007.09278/) (2020)
- [Context-Aware Image Matting for Simultaneous Foreground and Alpha Estimation](https://mlsanity.com/paper/1909.09725/) (2019)
- [Natural Image Matting via Guided Contextual Attention](https://mlsanity.com/paper/2001.04069/) (2020)
- [AttentionGAN: Unpaired Image-to-Image Translation using Attention-Guided Generative Adversarial Networks](https://mlsanity.com/paper/1911.11897/) (2019)
- [Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation](https://mlsanity.com/paper/1912.12215/) (2019)
- [Semantic Human Matting](https://mlsanity.com/paper/1809.01354/) (2018)
- [Information-Flow Matting](https://mlsanity.com/paper/1707.05055/) (2017)
- [AlphaGAN: Generative adversarial networks for natural image matting](https://mlsanity.com/paper/1807.10088/) (2018)
- [Disentangled Image Matting](https://mlsanity.com/paper/1909.04686/) (2019)
- [Dual Attention GANs for Semantic Image Synthesis](https://mlsanity.com/paper/2008.13024/) (2020)
- [Disentangled High Quality Salient Object Detection](https://mlsanity.com/paper/2108.03551/) (2021)
- [Bipartite Graph Reasoning GANs for Person Image Generation](https://mlsanity.com/paper/2008.04381/) (2020)
- [Fast Deep Matting for Portrait Animation on Mobile Phone](https://mlsanity.com/paper/1707.08289/) (2017)
- [Instance Segmentation based Semantic Matting for Compositing Applications](https://mlsanity.com/paper/1904.05457/) (2019)
- [CLASS: Cross-Level Attention and Supervision for Salient Objects Detection](https://mlsanity.com/paper/2009.10916/) (2020)

## Built on by (most cited first, 6 of 6)

- [Federated Learning with Label Distribution Skew via Logits Calibration](https://mlsanity.com/paper/2209.00189/) (2022)
- [Towards Practical Certifiable Patch Defense with Vision Transformer](https://mlsanity.com/paper/2203.08519/) (2022)
- [Deep Image Matting: A Comprehensive Survey](https://mlsanity.com/paper/2304.04672/) (2023)
- [Scene Matters: Model-based Deep Video Compression](https://mlsanity.com/paper/2303.04557/) (2023)
- [Towards Label-Efficient Human Matting: A Simple Baseline for Weakly Semi-Supervised Trimap-Free Human Matting](https://mlsanity.com/paper/2404.00921/) (2024)
- [Disentangle Saliency Detection into Cascaded Detail Modeling and Body Filling](https://mlsanity.com/paper/2202.04112/) (2022)

## Abstract (arXiv)

Over the last few years, deep learning based approaches have achieved outstanding improvements in natural image matting. However, there are still two drawbacks that impede the widespread application of image matting: the reliance on user-provided trimaps and the heavy model sizes. In this paper, we propose a trimap-free natural image matting method with a lightweight model. With a lightweight basic convolution block, we build a two-stages framework: Segmentation Network (SN) is designed to capture sufficient semantics and classify the pixels into unknown, foreground and background regions; Matting Refine Network (MRN) aims at capturing detailed texture information and regressing accurate alpha values. With the proposed cross-level fusion Module (CFM), SN can efficiently utilize multi-scale features with less computational cost. Efficient non-local attention module (ENA) in MRN can efficiently model the relevance between different pixels and help regress high-quality alpha values. Utilizing these techniques, we construct an extremely light-weighted model, which achieves comparable performance with ~1% parameters (344k) of large models on popular natural image matting benchmarks.