# Long-Range Feature Propagating for Natural Image Matting

arXiv: https://arxiv.org/abs/2109.12252 · HTML: https://mlsanity.com/paper/2109.12252/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 44 references that are in the map.
- First posted on arXiv 25 Sep 2021.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [Understanding the Effective Receptive Field in Deep Convolutional Neural Networks](https://mlsanity.com/paper/1701.04128/) (2017)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [Deep Image Matting](https://mlsanity.com/paper/1703.03872/) (2017)
- [Background Matting: The World is Your Green Screen](https://mlsanity.com/paper/2004.00626/) (2020)
- [Indices Matter: Learning to Index for Deep Image Matting](https://mlsanity.com/paper/1908.00672/) (2019)
- [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)
- [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)
- [High-Resolution Deep Image Matting](https://mlsanity.com/paper/2009.06613/) (2020)

## Built on by (most cited first, 13 of 13)

- [TransMatting: Enhancing Transparent Objects Matting with Transformers](https://mlsanity.com/paper/2208.03007/) (2022)
- [Deep Image Matting: A Comprehensive Survey](https://mlsanity.com/paper/2304.04672/) (2023)
- [Diffusion for Natural Image Matting](https://mlsanity.com/paper/2312.05915/) (2023)
- [Revisiting Context Aggregation for Image Matting](https://mlsanity.com/paper/2304.01171/) (2023)
- [dugMatting: Decomposed-Uncertainty-Guided Matting](https://mlsanity.com/paper/2306.01452/) (2023)
- [End-to-End Human Instance Matting](https://mlsanity.com/paper/2403.01510/) (2024)
- [Training Matting Models without Alpha Labels](https://mlsanity.com/paper/2408.10539/) (2024)
- [In-Context Matting](https://mlsanity.com/paper/2403.15789/) (2024)
- [Trimap-guided Feature Mining and Fusion Network for Natural Image Matting](https://mlsanity.com/paper/2112.00510/) (2021)
- [Improving Deep Image Matting via Local Smoothness Assumption](https://mlsanity.com/paper/2112.13809/) (2021)
- [TransMatting: Tri-token Equipped Transformer Model for Image Matting](https://mlsanity.com/paper/2303.06476/) (2023)
- [DART: Depth-Enhanced Accurate and Real-Time Background Matting](https://mlsanity.com/paper/2402.15820/) (2024)
- [Learning Multiple Representations with Inconsistency-Guided Detail Regularization for Mask-Guided Matting](https://mlsanity.com/paper/2403.19213/) (2024)

## Abstract (arXiv)

Natural image matting estimates the alpha values of unknown regions in the trimap. Recently, deep learning based methods propagate the alpha values from the known regions to unknown regions according to the similarity between them. However, we find that more than 50% pixels in the unknown regions cannot be correlated to pixels in known regions due to the limitation of small effective reception fields of common convolutional neural networks, which leads to inaccurate estimation when the pixels in the unknown regions cannot be inferred only with pixels in the reception fields. To solve this problem, we propose Long-Range Feature Propagating Network (LFPNet), which learns the long-range context features outside the reception fields for alpha matte estimation. Specifically, we first design the propagating module which extracts the context features from the downsampled image. Then, we present Center-Surround Pyramid Pooling (CSPP) that explicitly propagates the context features from the surrounding context image patch to the inner center image patch. Finally, we use the matting module which takes the image, trimap and context features to estimate the alpha matte. Experimental results demonstrate that the proposed method performs favorably against the state-of-the-art methods on the AlphaMatting and Adobe Image Matting datasets.