mlsanityData to 7 Oct 2026

Deep Image Matting: A Comprehensive Survey

arXiv 2304.04672 ↗First posted 10 Apr 2023cs.CVcited by 12

Not on the map. Why?

Abstract (arXiv)

Image matting refers to extracting precise alpha matte from natural images, and it plays a critical role in various downstream applications, such as image editing. Despite being an ill-posed problem, traditional methods have been trying to solve it for decades. The emergence of deep learning has revolutionized the field of image matting and given birth to multiple new techniques, including automatic, interactive, and referring image matting. This paper presents a comprehensive review of recent advancements in image matting in the era of deep learning. We focus on two fundamental sub-tasks: auxiliary input-based image matting, which involves user-defined input to predict the alpha matte, and automatic image matting, which generates results without any manual intervention. We systematically review the existing methods for these two tasks according to their task settings and network structures and provide a summary of their advantages and disadvantages. Furthermore, we introduce the commonly used image matting datasets and evaluate the performance of representative matting methods both quantitatively and qualitatively. Finally, we discuss relevant applications of image matting and highlight existing challenges and potential opportunities for future research. We also maintain a public repository to track the rapid development of deep image matting at https://github.com/JizhiziLi/matting-survey.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 105 of its 212 references that are in the map. First posted on arXiv 10 Apr 2023.

Builds on

105 of 212 references.

References · 105

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  5. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  6. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  7. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  8. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  9. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  10. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  11. 2017Mask R-CNNcited by 7,259
  12. 2016Densely Connected Convolutional Networkscited by 6,691
38 more
  1. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  2. 2022Generative Adversarial Networkscited by 5,603
  3. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  4. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  5. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  6. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
  7. 2016Pyramid Scene Parsing Networkcited by 2,472
  8. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,320
  9. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,345
  10. 2022Imagic: Text-Based Real Image Editing with Diffusion Modelscited by 1,172
  11. 2016Least Squares Generative Adversarial Networkscited by 1,000
  12. 2016COCO-Stuff: Thing and Stuff Classes in Contextcited by 858
  13. 2018MVSNet: Depth Inference for Unstructured Multi-view Stereocited by 780
  14. 2020Image Segmentation Using Deep Learning: A Surveycited by 659
  15. 2019Self-supervised Visual Feature Learning with Deep Neural Networks: A Surveycited by 656
  16. 2017The Perception-Distortion Tradeoffcited by 582
  17. 2016ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentationcited by 330
  18. 2021ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Biascited by 216
  19. 2021Concealed Object Detectioncited by 212
  20. 2019A Simple Pooling-Based Design for Real-Time Salient Object Detectioncited by 211
  21. 2019MirrorGAN: Learning Text-to-image Generation by Redescriptioncited by 176
  22. 2020Real-Time High-Resolution Background Mattingcited by 172
  23. 2017Deep Photo Style Transfercited by 168
  24. 2019Salient Object Detection in the Deep Learning Era: An In-Depth Surveycited by 163
  25. 2018Context-aware Synthesis for Video Frame Interpolationcited by 153
  26. 2019Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentationcited by 151
  27. 2017Deep Image Mattingcited by 150
  28. 2019Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolutioncited by 140
  29. 2021Learned Token Pruning for Transformerscited by 138
  30. 2018Fighting Fake News: Image Splice Detection via Learned Self-Consistencycited by 136
  31. 2018High Quality Monocular Depth Estimation via Transfer Learningcited by 133
  32. 2020MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decompositioncited by 127
  33. 2022ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyondcited by 117
  34. 2020Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Thingscited by 110
  35. 2020Learning Human-Object Interaction Detection using Interaction Pointscited by 108
  36. 2020Background Matting: The World is Your Green Screencited by 104
  37. 2014Salient Object Detection: A Surveycited by 98
  38. 2019Indices Matter: Learning to Index for Deep Image Mattingcited by 93

50 most cited of 105 All 105 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 5; 2025: 2; 2026: 3.

12 papers.

  1. 2024Depth Pro: Sharp Monocular Metric Depth in Less Than a Secondcited by 395
  2. 2023Towards Unified Deep Image Deraining: A Survey and A New Benchmarkcited by 34
  3. 2023Matting Anythingcited by 30
  4. 2024In-Context Mattingcited by 3
  5. 2024Towards Natural Image Matting in the Wild via Real-Scenario Priorcited by 3
  6. 2026Guardians of the Hair: Rescuing Soft Boundaries in Depth, Stereo, and Novel Viewscited by 2
  7. 2025Efficient Portrait Matte Creation With Layer Diffusion and Connectivity Priorscited by 1
  8. 2024A Cost-Efficient Approach for Creating Virtual Fitting Room using Generative Adversarial Networks (GANs)cited by 0
  9. 2024DART: Depth-Enhanced Accurate and Real-Time Background Mattingcited by 0
  10. 2025Capture Stage Matting: Challenges, Approaches, and Solutions for Offline and Real-Time Processingcited by 0
  11. 2026A Multi-Layer System for Ultra-High-Resolution Static 360-Degree Telepresencecited by 0
  12. 2026Enhancing Multi-Region Stylization with Interior-Guided Boundary Repaircited by 0