EdgeFlow: Achieving Practical Interactive Segmentation with Edge-Guided Flow

By Yuying Hao, Yi Liu, Zewu Wu, Lin Han, Yizhou Chen, Guowei Chen, Lutao Chu, Shiyu Tang, Zhiliang Yu, Zeyu Chen and 1 more. 2021

93 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Hongsheng Li
2. Peng Gao
3. Renrui Zhang
4. Xiaogang Wang
5. Junting Pan
6. Hengshuang Zhao
7. Ziyu Guo
8. Yu Qiao

Citations in the corpus

• FocalClick: Towards Practical Interactive Image Segmentation (2022, background): “EdgeFlow [14] and 99%AccuracyNet [10] make refinement for the boundary of the primitive predictions.”
• Personalize Segment Anything Model with One Shot (2023, methodology): “Prompt tuning (Lester et al., 2021; Zhou et al., 2022; Jia et al., 2022; Liu et al., 2021) suggests using learnable soft prompts alongside frozen models to perform specific downstream tasks, achieving more competitive performance with scale and robust domain transfer compared to full model tuning.”