Shift-Aware Calibration for Fine-Tuned CLIP: Leveraging Image-Text Alignment

Vision-language models (VLMs), such as CLIP, adapt effectively to downstream tasks through prompt tuning, but fine-tuning can misalign predictive confidence and accuracy, particularly on unseen classes. Existing VLM-specific calibration methods mainly rely on textual features of train classes, limiting their applicability across class and distribution shifts. We propose Shift-Aware Calibration (SAC), a training-free, sample-wise method that uses the discrepancy between the output logits of the original and fine-tuned CLIP as a calibration signal. We explicitly call this quantity logit shift, since it is measured in prediction space rather than between hidden representations. SAC maps this logit shift to a positive scaling factor that adjusts confidence while preserving the predicted class. Across 11 datasets and five fine-tuning methods, SAC improves calibration on train and unseen classes and under cross-dataset and domain-generalization evaluation. Mutual Information (MI) analysis shows a statistical dependency between the logit-shift signal and ECE across methods and datasets, providing empirical evidence that the signal used by SAC is informative for calibration and supporting the effectiveness of its shift-aware design.