Single Image Test-Time Adaptation for Segmentation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911941392334848 |
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| author | Janouskova, Klara Shor, Tamir Baskin, Chaim Matas, Jiri |
| author_facet | Janouskova, Klara Shor, Tamir Baskin, Chaim Matas, Jiri |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2309_14052 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Single Image Test-Time Adaptation for Segmentation Janouskova, Klara Shor, Tamir Baskin, Chaim Matas, Jiri Computer Vision and Pattern Recognition 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. |
| title | Single Image Test-Time Adaptation for Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2309.14052 |