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We propose a test-time adaptation method for cross-domain image segmentation.Our method is simple: Given a new unseen instance at test time, we adapt apre-trained model by conducting instance-specific BatchNorm (statistics)calibration. Our approach has two core components. First, we replace themanually designed BatchNorm calibration rule with a learnable module. Second,we leverage strong data augmentation to simulate random domain shifts forlearning the calibration rule. In contrast to existing domain adaptationmethods, our method does not require accessing the target domain data attraining time or conducting computationally expensive test-time modeltraining/optimization. Equipping our method with models trained by standardrecipes achieves significant improvement, comparing favorably with severalstate-of-the-art domain generalization and one-shot unsupervised domainadaptation approaches. Combining our method with the domain generalizationmethods further improves performance, reaching a new state of the art.

Learning Instance-Specific Adaptation for Cross-Domain Segmentation