The goal of click-based interactive image segmentation is to obtain precise object segmentation masks with limited user interaction, i.e., by a minimal number of user clicks. Existing methods require users to provide all the clicks: by first inspecting the segmentation mask and then providing points on mislabeled regions, iteratively. We ask the question: can our model directly predict where to click, so as to further reduce the user interaction cost? To this end, we propose {\PseudoClick}, a generic framework that enables existing segmentation networks to propose candidate next clicks. These automatically generated clicks, termed pseudo clicks in this work, serve as an imitation of human clicks to refine the segmentation mask.
@article{arxiv.2207.05282,
title = {PseudoClick: Interactive Image Segmentation with Click Imitation},
author = {Qin Liu and Meng Zheng and Benjamin Planche and Srikrishna Karanam and Terrence Chen and Marc Niethammer and Ziyan Wu},
journal= {arXiv preprint arXiv:2207.05282},
year = {2022}
}