Rethinking the Test-Time Prompt Tuning Objective from the Perspective of Calibration

arXiv:2608.30230 · cs.AI · Submitted 2026-08-31 · Read on arXiv

cs.AI

Submitted: 2026-08-31

Updated: 2026-09-08

Comments: 9 pages

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Calibration — 65× in this paper · explained in 7 episodes
• Accuracy — 32× in this paper · explained in 1 episode
• Test-time Prompt Tuning — 17× in this paper
• Adaptation — 14× in this paper · explained in 1 episode
• Regularization — 8× in this paper · explained in 3 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Distribution Shift — 5× in this paper · explained in 5 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Ablation study — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Embeddings — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• In International Conference on Learning Representations — 2× in this paper
• Robustness — 2× in this paper · explained in 5 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes
• Zero-Shot Generalization — 1× in this paper · explained in 5 episodes

Sources

• Dual-Modality Anchor-Guided Filtering for Test-time Prompt Tuning
• Learning Transferable Visual Models From Natural Language Supervision
• Efficient Test-Time Prompt Tuning for Vision-Language Models

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