Predictive Spectral Calibration for Source-Free Test-Time Regression
This work addresses a domain-specific problem in computer vision for researchers and practitioners dealing with regression tasks under distribution shifts, presenting an incremental advancement over existing subspace alignment methods.
The paper tackles the problem of test-time adaptation for image regression, which has been less studied than classification, by proposing a source-free framework that improves performance under distribution shifts. The method shows consistent improvements over strong baselines, with clear gains in severe shift scenarios.
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.