Single Image Test-Time Adaptation for Segmentation

• URL: http://arxiv.org/abs/2309.14052v2
• Date: Tue, 2 Jul 2024 19:34:23 GMT
• Title: Single Image Test-Time Adaptation for Segmentation
• Authors: Klara Janouskova, Tamir Shor, Chaim Baskin, Jiri Matas,
• Abstract summary: 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. Our additions to the baselines result in a 3.51 and 3.28 % increase over non-adapted baselines.
• Score: 22.600586011303363
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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