Test-Time Adaptation (TTA) methods improve the robustness of deep neural networks to domain shift on a variety of tasks such as image classification or segmentation. This work explores adapting segmentation models to a single unlabelled image with no other data available at test-time. In particular, this work focuses on adaptation by optimizing self-supervised losses at test-time. Multiple baselines based on different principles are evaluated under diverse conditions and a novel adversarial training is introduced for adaptation with mask refinement. Our additions to the baselines result in a 3.51 and 3.28 % increase over non-adapted baselines, without these improvements, the increase would be 1.7 and 2.16 % only.

翻译：测试时自适应（TTA）方法能提升深度神经网络在多种任务（如图像分类或分割）中对领域偏移的鲁棒性。本研究探索了在测试阶段仅利用单张无标签图像（无其他可用数据）对分割模型进行自适应的方法。具体而言，本工作聚焦于通过优化测试时自监督损失实现自适应。基于不同原理的多种基线方法在不同条件下进行了评估，并引入了一种新颖的对抗训练机制，通过掩码优化实现自适应。我们对基线方法的改进使性能相比未自适应基线分别提升了3.51%和3.28%，而未采用这些改进时，提升幅度仅为1.7%和2.16%。