PseudoClick: Interactive Image Segmentation with Click Imitation
This work addresses the need for more efficient interactive segmentation tools for users in computer vision applications, though it appears incremental as it builds on existing segmentation networks.
The paper tackles the problem of reducing user interaction cost in click-based interactive image segmentation by proposing a framework that predicts candidate next clicks automatically, termed pseudo clicks, to refine segmentation masks without requiring users to provide all clicks.
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.