Continuous Adaptation for Interactive Segmentation Using Teacher-Student Architecture

Interactive segmentation is the task of segmenting objects or regions of interest from images based on user annotations. While most current methods perform effectively on images from the same distribution as the training dataset, they suffer to generalize on unseen domains. To address this issue some approaches incorporate test-time adaptation techniques which, on the other hand, may lead to catastrophic forgetting (i.e. degrading the performance on the previously seen domains) when applied on datasets from various domains sequentially. In this paper, we propose a novel domain adaptation approach leveraging a teacher-student learning framework to tackle the catastrophic forgetting issue. Continuously updating the student and teacher models based on user clicks results in improved segmentation accuracy on unseen domains, while preserving comparable performance on previous domains. Our approach is evaluated on a sequence of datasets from unseen domains (i.e. medical, aerial images, etc.), and, after adaptation, on the source domain demonstrating a significant decline of catastrophic forgetting (e.g. from 55% to 4% on Berkeley dataset).