  Interactive image segmentation aims at segmenting a target region through a
way of human-computer interaction. Recent works based on deep learning have
achieved excellent performance, while most of them focus on improving the
accuracy of the training set and ignore potential improvement on the test set.
In the inference phase, they tend to have a good performance on similar domains
to the training set, and lack adaptability to domain shift, so they require
more user efforts to obtain satisfactory results. In this work, we propose
RAIS, a robust and accurate architecture for interactive segmentation with
continuous learning, where the model can learn from both train and test data
sets. For efficient learning on the test set, we propose a novel optimization
strategy to update global and local parameters with a basic segmentation module
and adaptation module, respectively. Moreover, we perform extensive experiments
on several benchmarks that show our method can handle data distribution shifts
and achieves SOTA performance compared with recent interactive segmentation
methods. Besides, our method also shows its robustness in the datasets of
remote sensing and medical imaging where the data domains are completely
different between training and testing.
