# Improved Image Matting via Real-time User Clicks and Uncertainty Estimation

arXiv: https://arxiv.org/abs/2012.08323 · HTML: https://mlsanity.com/paper/2012.08323/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2020.

## Built on by (most cited first, 22 of 22)

- [Matte Anything: Interactive Natural Image Matting with Segment Anything Models](https://mlsanity.com/paper/2306.04121/) (2023)
- [Matting Anything](https://mlsanity.com/paper/2306.05399/) (2023)
- [Referring Image Matting](https://mlsanity.com/paper/2206.05149/) (2022)
- [ZIM: Zero-Shot Image Matting for Anything](https://mlsanity.com/paper/2411.00626/) (2024)
- [Matting by Generation](https://mlsanity.com/paper/2407.21017/) (2024)
- [Deep Image Matting: A Comprehensive Survey](https://mlsanity.com/paper/2304.04672/) (2023)
- [Deep Image Matting with Flexible Guidance Input](https://mlsanity.com/paper/2110.10898/) (2021)
- [Towards Content-based Pixel Retrieval in Revisited Oxford and Paris](https://mlsanity.com/paper/2309.05438/) (2023)
- [Hierarchical and Progressive Image Matting](https://mlsanity.com/paper/2210.06906/) (2022)
- [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)
- [SDMatte: Grafting Diffusion Models for Interactive Matting](https://mlsanity.com/paper/2508.00443/) (2025)
- [Alpha Matte Generation from Single Input for Portrait Matting](https://mlsanity.com/paper/2106.03210/) (2021)
- [Learning Trimaps via Clicks for Image Matting](https://mlsanity.com/paper/2404.00335/) (2024)
- [Towards Label-Efficient Human Matting: A Simple Baseline for Weakly Semi-Supervised Trimap-Free Human Matting](https://mlsanity.com/paper/2404.00921/) (2024)
- [Exploring the Interactive Guidance for Unified and Effective Image Matting](https://mlsanity.com/paper/2205.08324/) (2022)
- [DFIMat: Decoupled Flexible Interactive Matting in Multi-Person Scenarios](https://mlsanity.com/paper/2410.09788/) (2024)
- [Semantic-guided Automatic Natural Image Matting with Trimap Generation Network and Light-weight Non-local Attention](https://mlsanity.com/paper/2103.17020/) (2021)
- [Wider and Higher: Intensive Integration and Global Foreground Perception for Image Matting](https://mlsanity.com/paper/2210.06919/) (2022)
- [SAM2Matting: Generalized Image and Video Matting](https://mlsanity.com/paper/2606.27339/) (2026)
- [Self-supervised Automatic Matting](https://mlsanity.com/paper/2607.10395/) (2026)

## Abstract (arXiv)

Image matting is a fundamental and challenging problem in computer vision and graphics. Most existing matting methods leverage a user-supplied trimap as an auxiliary input to produce good alpha matte. However, obtaining high-quality trimap itself is arduous, thus restricting the application of these methods. Recently, some trimap-free methods have emerged, however, the matting quality is still far behind the trimap-based methods. The main reason is that, without the trimap guidance in some cases, the target network is ambiguous about which is the foreground target. In fact, choosing the foreground is a subjective procedure and depends on the user's intention. To this end, this paper proposes an improved deep image matting framework which is trimap-free and only needs several user click interactions to eliminate the ambiguity. Moreover, we introduce a new uncertainty estimation module that can predict which parts need polishing and a following local refinement module. Based on the computation budget, users can choose how many local parts to improve with the uncertainty guidance. Quantitative and qualitative results show that our method performs better than existing trimap-free methods and comparably to state-of-the-art trimap-based methods with minimal user effort.