  Test-time adaptation (TTA) adapts the pre-trained models during inference
using unlabeled test data and has received a lot of research attention due to
its potential practical value. Unfortunately, without any label supervision,
existing TTA methods rely heavily on heuristic or empirical studies. Where to
update the model always falls into suboptimal or brings more computational
resource consumption. Meanwhile, there is still a significant performance gap
between the TTA approaches and their supervised counterparts. Motivated by
active learning, in this work, we propose the active test-time adaptation for
semantic segmentation setup. Specifically, we introduce the human-in-the-loop
pattern during the testing phase, which queries very few labels to facilitate
predictions and model updates in an online manner. To do so, we propose a
simple but effective ATASeg framework, which consists of two parts, i.e., model
adapter and label annotator. Extensive experiments demonstrate that ATASeg
bridges the performance gap between TTA methods and their supervised
counterparts with only extremely few annotations, even one click for labeling
surpasses known SOTA TTA methods by 2.6% average mIoU on ACDC benchmark.
Empirical results imply that progress in either the model adapter or the label
annotator will bring improvements to the ATASeg framework, giving it large
research and reality potential.

Computer Vision and Pattern Recognition