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PseudoClick: Interactive Image Segmentation with Click Imitation
Qin Liu1,2, Meng Zheng2, Benjamin Planche2
1University of North Carolina at Chapel Hill, Chapel Hill NC, USA.
Computer Vision - ECCV ... : ... European Conference on Computer Vision : Proceedings. European Conference on Computer Vision
|December 10, 2025
Summary
This study introduces PseudoClick, a framework for click-based interactive image segmentation that predicts optimal user clicks. This approach significantly reduces interaction costs and improves segmentation accuracy.
Area of Science:
- Computer Vision
- Machine Learning
- Image Processing
Background:
- Click-based interactive image segmentation aims for precise object masks with minimal user input.
- Current methods require iterative user correction, increasing interaction costs.
- The need exists to reduce user effort in interactive segmentation.
Purpose of the Study:
- To develop a method that automatically predicts the next best user click for interactive image segmentation.
- To reduce the overall number of user interactions required for accurate segmentation.
- To enhance existing segmentation networks with click prediction capabilities.
Main Methods:
- Proposed PseudoClick, a generic framework to enable segmentation networks to predict candidate next clicks.
- Implemented pseudo-clicks as an imitation of human clicks to refine segmentation masks.
- Integrated PseudoClick with existing segmentation backbones.
Main Results:
- Achieved state-of-the-art results on several popular benchmarks.
- Demonstrated strong generalization capabilities across different domains and modalities.
- Significantly outperformed existing methods, e.g., reducing clicks by 12.4% for 85% IoU on the Pascal dataset.
Conclusions:
- PseudoClick effectively reduces user interaction costs in click-based image segmentation.
- The proposed click prediction mechanism leads to improved segmentation performance.
- PseudoClick offers a promising direction for more efficient interactive image segmentation systems.
