# EdgeFlow: Achieving Practical Interactive Segmentation with Edge-Guided Flow

arXiv: https://arxiv.org/abs/2109.09406 · HTML: https://mlsanity.com/paper/2109.09406/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 54 references that are in the map.
- First posted on arXiv 20 Sep 2021.

## Builds on (most cited first, 18 of 18)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [LVIS: A Dataset for Large Vocabulary Instance Segmentation](https://mlsanity.com/paper/1908.03195/) (2019)
- [Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation](https://mlsanity.com/paper/1909.11065/) (2019)
- [Reviving Iterative Training with Mask Guidance for Interactive Segmentation](https://mlsanity.com/paper/2102.06583/) (2021)
- [Deep Interactive Object Selection](https://mlsanity.com/paper/1603.04042/) (2016)
- [Semantic Flow for Fast and Accurate Scene Parsing](https://mlsanity.com/paper/2002.10120/) (2020)
- [Deep Extreme Cut: From Extreme Points to Object Segmentation](https://mlsanity.com/paper/1711.09081/) (2017)
- [f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation](https://mlsanity.com/paper/2001.10331/) (2020)
- [Large-scale interactive object segmentation with human annotators](https://mlsanity.com/paper/1903.10830/) (2019)
- [Improving Semantic Segmentation via Decoupled Body and Edge Supervision](https://mlsanity.com/paper/2007.10035/) (2020)
- [Iteratively Trained Interactive Segmentation](https://mlsanity.com/paper/1805.04398/) (2018)
- [Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections](https://mlsanity.com/paper/1911.12709/) (2019)
- [PaddleSeg: A High-Efficient Development Toolkit for Image Segmentation](https://mlsanity.com/paper/2101.06175/) (2021)
- [Deep GrabCut for Object Selection](https://mlsanity.com/paper/1707.00243/) (2017)
- [PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation](https://mlsanity.com/paper/2103.06564/) (2021)

## Built on by (most cited first, 30 of 40)

- [Personalize Segment Anything Model with One Shot](https://mlsanity.com/paper/2305.03048/) (2023)
- [SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting with Neural Radiance Fields](https://mlsanity.com/paper/2211.12254/) (2022)
- [FocalClick: Towards Practical Interactive Image Segmentation](https://mlsanity.com/paper/2204.02574/) (2022)
- [LVOS: A Benchmark for Long-term Video Object Segmentation](https://mlsanity.com/paper/2211.10181/) (2022)
- [LVOS: A Benchmark for Large-scale Long-term Video Object Segmentation](https://mlsanity.com/paper/2404.19326/) (2024)
- [OR-NeRF: Object Removing from 3D Scenes Guided by Multiview Segmentation with Neural Radiance Fields](https://mlsanity.com/paper/2305.10503/) (2023)
- [PP-LiteSeg: A Superior Real-Time Semantic Segmentation Model](https://mlsanity.com/paper/2204.02681/) (2022)
- [3D Gaussian Inpainting with Depth-Guided Cross-View Consistency](https://mlsanity.com/paper/2502.11801/) (2025)
- [InterFormer: Real-time Interactive Image Segmentation](https://mlsanity.com/paper/2304.02942/) (2023)
- [Focused and Collaborative Feedback Integration for Interactive Image Segmentation](https://mlsanity.com/paper/2303.11880/) (2023)
- [DynaMITe: Dynamic Query Bootstrapping for Multi-object Interactive Segmentation Transformer](https://mlsanity.com/paper/2304.06668/) (2023)
- [AdaptiveClick: Clicks-aware Transformer with Adaptive Focal Loss for Interactive Image Segmentation](https://mlsanity.com/paper/2305.04276/) (2023)
- [EISeg: An Efficient Interactive Segmentation Tool based on PaddlePaddle](https://mlsanity.com/paper/2210.08788/) (2022)
- [PP-HumanSeg: Connectivity-Aware Portrait Segmentation with a Large-Scale Teleconferencing Video Dataset](https://mlsanity.com/paper/2112.07146/) (2021)
- [Multi-granularity Interaction Simulation for Unsupervised Interactive Segmentation](https://mlsanity.com/paper/2303.13399/) (2023)
- [Adapting the Segment Anything Model During Usage in Novel Situations](https://mlsanity.com/paper/2404.08421/) (2024)
- [PaintSeg: Training-free Segmentation via Painting](https://mlsanity.com/paper/2305.19406/) (2023)
- [WildSeg3D: Segment Any 3D Objects in the Wild from 2D Images](https://mlsanity.com/paper/2503.08407/) (2025)
- [Impact of Pseudo Depth on Open World Object Segmentation with Minimal User Guidance](https://mlsanity.com/paper/2304.05716/) (2023)
- [FocSAM: Delving Deeply into Focused Objects in Segmenting Anything](https://mlsanity.com/paper/2405.18706/) (2024)
- [Progressive Learning with Cross-Window Consistency for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2211.12425/) (2022)
- [Contour-based Interactive Segmentation](https://mlsanity.com/paper/2302.06353/) (2023)
- [Interactive Segmentation for Diverse Gesture Types Without Context](https://mlsanity.com/paper/2307.10518/) (2023)
- [iDet3D: Towards Efficient Interactive Object Detection for LiDAR Point Clouds](https://mlsanity.com/paper/2312.15449/) (2023)
- [Refining Segmentation On-the-Fly: An Interactive Framework for Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2403.06401/) (2024)
- [Exploring the Interactive Guidance for Unified and Effective Image Matting](https://mlsanity.com/paper/2205.08324/) (2022)
- [RAIS: Robust and Accurate Interactive Segmentation via Continual Learning](https://mlsanity.com/paper/2210.10984/) (2022)
- [MST: Adaptive Multi-Scale Tokens Guided Interactive Segmentation](https://mlsanity.com/paper/2401.04403/) (2024)
- [Structured Click Control in Transformer-based Interactive Segmentation](https://mlsanity.com/paper/2405.04009/) (2024)
- [SAM-REF: Introducing Image-Prompt Synergy during Interaction for Detail Enhancement in the Segment Anything Model](https://mlsanity.com/paper/2408.11535/) (2024)

## Abstract (arXiv)

High-quality training data play a key role in image segmentation tasks. Usually, pixel-level annotations are expensive, laborious and time-consuming for the large volume of training data. To reduce labelling cost and improve segmentation quality, interactive segmentation methods have been proposed, which provide the result with just a few clicks. However, their performance does not meet the requirements of practical segmentation tasks in terms of speed and accuracy. In this work, we propose EdgeFlow, a novel architecture that fully utilizes interactive information of user clicks with edge-guided flow. Our method achieves state-of-the-art performance without any post-processing or iterative optimization scheme. Comprehensive experiments on benchmarks also demonstrate the superiority of our method. In addition, with the proposed method, we develop an efficient interactive segmentation tool for practical data annotation tasks. The source code and tool is avaliable at https://github.com/PaddlePaddle/PaddleSeg.