# Hierarchical and Progressive Image Matting

arXiv: https://arxiv.org/abs/2210.06906 · HTML: https://mlsanity.com/paper/2210.06906/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 62 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 30 of 31)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning](https://mlsanity.com/paper/1611.05594/) (2016)
- [Deeply supervised salient object detection with short connections](https://mlsanity.com/paper/1611.04849/) (2016)
- [ParseNet: Looking Wider to See Better](https://mlsanity.com/paper/1506.04579/) (2015)
- [Real-Time High-Resolution Background Matting](https://mlsanity.com/paper/2012.07810/) (2020)
- [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)
- [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)
- [Semantic Human Matting](https://mlsanity.com/paper/1809.01354/) (2018)
- [Information-Flow Matting](https://mlsanity.com/paper/1707.05055/) (2017)
- [Semantic Image Matting](https://mlsanity.com/paper/2104.08201/) (2021)
- [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)
- [Learning Affinity-Aware Upsampling for Deep Image Matting](https://mlsanity.com/paper/2011.14288/) (2020)
- [High-Resolution Deep Image Matting](https://mlsanity.com/paper/2009.06613/) (2020)
- [CRRN: Multi-Scale Guided Concurrent Reflection Removal Network](https://mlsanity.com/paper/1805.11802/) (2018)
- [Improved Image Matting via Real-time User Clicks and Uncertainty Estimation](https://mlsanity.com/paper/2012.08323/) (2020)
- [Learning to Detect Instance-level Salient Objects Using Complementary Image Labels](https://mlsanity.com/paper/2111.10137/) (2021)
- [Real or Not Real, that is the Question](https://mlsanity.com/paper/2002.05512/) (2020)
- [Weakly-supervised Salient Instance Detection](https://mlsanity.com/paper/2009.13898/) (2020)
- [Smart Scribbles for Image Mating](https://mlsanity.com/paper/2103.17062/) (2021)
- [Multi-scale Information Assembly for Image Matting](https://mlsanity.com/paper/2101.02391/) (2021)

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

- [ViTMatte: Boosting Image Matting with Pretrained Plain Vision Transformers](https://mlsanity.com/paper/2305.15272/) (2023)
- [Revisiting Context Aggregation for Image Matting](https://mlsanity.com/paper/2304.01171/) (2023)
- [Efficient Portrait Matte Creation With Layer Diffusion and Connectivity Priors](https://mlsanity.com/paper/2501.16147/) (2025)
- [Dual-Context Aggregation for Universal Image Matting](https://mlsanity.com/paper/2402.18109/) (2024)

## Abstract (arXiv)

Most matting researches resort to advanced semantics to achieve high-quality alpha mattes, and direct low-level features combination is usually explored to complement alpha details. However, we argue that appearance-agnostic integration can only provide biased foreground details and alpha mattes require different-level feature aggregation for better pixel-wise opacity perception. In this paper, we propose an end-to-end Hierarchical and Progressive Attention Matting Network (HAttMatting++), which can better predict the opacity of the foreground from single RGB images without additional input. Specifically, we utilize channel-wise attention to distill pyramidal features and employ spatial attention at different levels to filter appearance cues. This progressive attention mechanism can estimate alpha mattes from adaptive semantics and semantics-indicated boundaries. We also introduce a hybrid loss function fusing Structural SIMilarity (SSIM), Mean Square Error (MSE), Adversarial loss, and sentry supervision to guide the network to further improve the overall foreground structure. Besides, we construct a large-scale and challenging image matting dataset comprised of 59, 600 training images and 1000 test images (a total of 646 distinct foreground alpha mattes), which can further improve the robustness of our hierarchical and progressive aggregation model. Extensive experiments demonstrate that the proposed HAttMatting++ can capture sophisticated foreground structures and achieve state-of-the-art performance with single RGB images as input.