Computer Science > Computer Vision and Pattern Recognition
arXiv:2603.09338 (cs)
[Submitted on 10 Mar 2026]
Title:Predictive Spectral Calibration for Source-Free Test-Time Regression
View a PDF of the paper titled Predictive Spectral Calibration for Source-Free Test-Time Regression, by Nguyen Viet Tuan Kiet and 2 other authors
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Abstract:Test-time adaptation (TTA) for image regression has received far less attention than its classification counterpart. Methods designed for classification often depend on classification-specific objectives and decision boundaries, making them difficult to transfer directly to continuous regression targets. Recent progress revisits regression TTA through subspace alignment, showing that simple source-guided alignment can be both practical and effective. Building on this line of work, we propose Predictive Spectral Calibration (PSC), a source-free framework that extends subspace alignment to block spectral matching. Instead of relying on a fixed support subspace alone, PSC jointly aligns target features within the source predictive support and calibrates residual spectral slack in the orthogonal complement. PSC remains simple to implement, model-agnostic, and compatible with off-the-shelf pretrained regressors. Experiments on multiple image regression benchmarks show consistent improvements over strong baselines, with particularly clear gains under severe distribution shifts.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2603.09338 [cs.CV] |
| (or arXiv:2603.09338v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09338
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From: Tuan Kiet Nguyen Viet [view email][v1] Tue, 10 Mar 2026 08:15:44 UTC (26 KB)
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View a PDF of the paper titled Predictive Spectral Calibration for Source-Free Test-Time Regression, by Nguyen Viet Tuan Kiet and 2 other authors
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