Instance-Aware Test-Time Segmentation for Continual Domain Shifts
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909951807455232 |
|---|---|
| author | Lee, Seunghwan Jung, Inyoung Lee, Hojoon Park, Eunil Hong, Sungeun |
| author_facet | Lee, Seunghwan Jung, Inyoung Lee, Hojoon Park, Eunil Hong, Sungeun |
| contents | Continual Test-Time Adaptation (CTTA) enables pre-trained models to adapt to continuously evolving domains. Existing methods have improved robustness but typically rely on fixed or batch-level thresholds, which cannot account for varying difficulty across classes and instances. This limitation is especially problematic in semantic segmentation, where each image requires dense, multi-class predictions. We propose an approach that adaptively adjusts pseudo labels to reflect the confidence distribution within each image and dynamically balances learning toward classes most affected by domain shifts. This fine-grained, class- and instance-aware adaptation produces more reliable supervision and mitigates error accumulation throughout continual adaptation. Extensive experiments across eight CTTA and TTA scenarios, including synthetic-to-real and long-term shifts, show that our method consistently outperforms state-of-the-art techniques, setting a new standard for semantic segmentation under evolving conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_08569 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Instance-Aware Test-Time Segmentation for Continual Domain Shifts Lee, Seunghwan Jung, Inyoung Lee, Hojoon Park, Eunil Hong, Sungeun Computer Vision and Pattern Recognition Continual Test-Time Adaptation (CTTA) enables pre-trained models to adapt to continuously evolving domains. Existing methods have improved robustness but typically rely on fixed or batch-level thresholds, which cannot account for varying difficulty across classes and instances. This limitation is especially problematic in semantic segmentation, where each image requires dense, multi-class predictions. We propose an approach that adaptively adjusts pseudo labels to reflect the confidence distribution within each image and dynamically balances learning toward classes most affected by domain shifts. This fine-grained, class- and instance-aware adaptation produces more reliable supervision and mitigates error accumulation throughout continual adaptation. Extensive experiments across eight CTTA and TTA scenarios, including synthetic-to-real and long-term shifts, show that our method consistently outperforms state-of-the-art techniques, setting a new standard for semantic segmentation under evolving conditions. |
| title | Instance-Aware Test-Time Segmentation for Continual Domain Shifts |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.08569 |