  Interactive segmentation allows users to extract target masks by making
positive/negative clicks. Although explored by many previous works, there is
still a gap between academic approaches and industrial needs: first, existing
models are not efficient enough to work on low power devices; second, they
perform poorly when used to refine preexisting masks as they could not avoid
destroying the correct part. FocalClick solves both issues at once by
predicting and updating the mask in localized areas. For higher efficiency, we
decompose the slow prediction on the entire image into two fast inferences on
small crops: a coarse segmentation on the Target Crop, and a local refinement
on the Focus Crop. To make the model work with preexisting masks, we formulate
a sub-task termed Interactive Mask Correction, and propose Progressive Merge as
the solution. Progressive Merge exploits morphological information to decide
where to preserve and where to update, enabling users to refine any preexisting
mask effectively. FocalClick achieves competitive results against SOTA methods
with significantly smaller FLOPs. It also shows significant superiority when
making corrections on preexisting masks. Code and data will be released at
github.com/XavierCHEN34/ClickSEG
