Instance-Aware Test-Time Segmentation for Continual Domain Shifts

• URL: http://arxiv.org/abs/2512.08569v1
• Date: Tue, 09 Dec 2025 13:06:15 GMT
• Title: Instance-Aware Test-Time Segmentation for Continual Domain Shifts
• Authors: Seunghwan Lee, Inyoung Jung, Hojoon Lee, Eunil Park, Sungeun Hong,
• Abstract summary: Continual Test-Time Adaptation (CTTA) enables pre-trained models to adapt to continuously evolving domains.<n>We propose an approach that adaptively adjusts pseudo labels to reflect the confidence distribution within each image.<n>This fine-grained, class- and instance-aware adaptation produces more reliable supervision and mitigates error accumulation.
• Score: 19.919913865727995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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