  Interactive image segmentation aims at obtaining a segmentation mask for an
image using simple user annotations. During each round of interaction, the
segmentation result from the previous round serves as feedback to guide the
user's annotation and provides dense prior information for the segmentation
model, effectively acting as a bridge between interactions. Existing methods
overlook the importance of feedback or simply concatenate it with the original
input, leading to underutilization of feedback and an increase in the number of
required annotations. To address this, we propose an approach called Focused
and Collaborative Feedback Integration (FCFI) to fully exploit the feedback for
click-based interactive image segmentation. FCFI first focuses on a local area
around the new click and corrects the feedback based on the similarities of
high-level features. It then alternately and collaboratively updates the
feedback and deep features to integrate the feedback into the features. The
efficacy and efficiency of FCFI were validated on four benchmarks, namely
GrabCut, Berkeley, SBD, and DAVIS. Experimental results show that FCFI achieved
new state-of-the-art performance with less computational overhead than previous
methods. The source code is available at
https://github.com/veizgyauzgyauz/FCFI.
