# Boosting General Trimap-free Matting in the Real-World Image

arXiv: https://arxiv.org/abs/2405.17916 · HTML: https://mlsanity.com/paper/2405.17916/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 82 references that are in the map.
- First posted on arXiv 28 May 2024.

## Builds on (most cited first, 30 of 37)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://mlsanity.com/paper/1706.02677/) (2017)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://mlsanity.com/paper/2005.09007/) (2020)
- [EGNet:Edge Guidance Network for Salient Object Detection](https://mlsanity.com/paper/1908.08297/) (2019)
- [A Simple Pooling-Based Design for Real-Time Salient Object Detection](https://mlsanity.com/paper/1904.09569/) (2019)
- [Multi-scale Interactive Network for Salient Object Detection](https://mlsanity.com/paper/2007.09062/) (2020)
- [MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition](https://mlsanity.com/paper/2011.11961/) (2020)
- [Global Context-Aware Progressive Aggregation Network for Salient Object Detection](https://mlsanity.com/paper/2003.00651/) (2020)
- [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)
- [Mask Guided Matting via Progressive Refinement Network](https://mlsanity.com/paper/2012.06722/) (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)
- [Region-aware Adaptive Instance Normalization for Image Harmonization](https://mlsanity.com/paper/2106.02853/) (2021)
- [Semantic Human Matting](https://mlsanity.com/paper/1809.01354/) (2018)
- [Making Images Real Again: A Comprehensive Survey on Deep Image Composition](https://mlsanity.com/paper/2106.14490/) (2021)
- [Information-Flow Matting](https://mlsanity.com/paper/1707.05055/) (2017)
- [Deep Automatic Natural Image Matting](https://mlsanity.com/paper/2107.07235/) (2021)
- [Semantic Image Matting](https://mlsanity.com/paper/2104.08201/) (2021)
- [Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images](https://mlsanity.com/paper/1905.06368/) (2019)
- [AlphaGAN: Generative adversarial networks for natural image matting](https://mlsanity.com/paper/1807.10088/) (2018)
- [Boosting Semantic Human Matting with Coarse Annotations](https://mlsanity.com/paper/2004.04955/) (2020)

## Built on by (most cited first, 4 of 4)

- [GDGS: 3D Gaussian Splatting Via Geometry-Guided Initialization And Dynamic Density Control](https://mlsanity.com/paper/2507.00363/) (2025)
- [Capture Stage Matting: Challenges, Approaches, and Solutions for Offline and Real-Time Processing](https://mlsanity.com/paper/2507.07623/) (2025)
- [Segment and Matte Anything in a Unified Model](https://mlsanity.com/paper/2601.12147/) (2026)
- [MAViE: A Multi-scale Adaptive Vision Encoder for Fine-grained Visual Perception and Efficient Multimodal Reasoning](https://mlsanity.com/paper/2607.24424/) (2026)

## Abstract (arXiv)

Image matting aims to obtain an alpha matte that separates foreground objects from the background accurately. Recently, trimap-free matting has been well studied because it requires only the original image without any extra input. Such methods usually extract a rough foreground by itself to take place trimap as further guidance. However, the definition of 'foreground' lacks a unified standard and thus ambiguities arise. Besides, the extracted foreground is sometimes incomplete due to inadequate network design. Most importantly, there is not a large-scale real-world matting dataset, and current trimap-free methods trained with synthetic images suffer from large domain shift problems in practice. In this paper, we define the salient object as foreground, which is consistent with human cognition and annotations of the current matting dataset. Meanwhile, data and technologies in salient object detection can be transferred to matting in a breeze. To obtain a more accurate and complete alpha matte, we propose a network called Multi-Feature fusion-based Coarse-to-fine Network (MFC-Net), which fully integrates multiple features for an accurate and complete alpha matte. Furthermore, we introduce image harmony in data composition to bridge the gap between synthetic and real images. More importantly, we establish the largest general matting dataset (Real-19k) in the real world to date. Experiments show that our method is significantly effective on both synthetic and real-world images, and the performance in the real-world dataset is far better than existing matting-free methods. Our code and data will be released soon.