# Prompting without Panic: Attribute-aware, Zero-shot, Test-Time Calibration

arXiv: https://arxiv.org/abs/2506.22819 · HTML: https://mlsanity.com/paper/2506.22819/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Prompt learning and adaptation of CLIP, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 43 of its 61 references that are in the map.
- First posted on arXiv 28 Jun 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Prompt learning and adaptation of CLIP](https://mlsanity.com/mllm/prompt-learning-and-adaptation-of-clip/)

## Builds on (most cited first, 30 of 43)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization](https://mlsanity.com/paper/2006.16241/) (2020)
- [Fine-Grained Visual Classification of Aircraft](https://mlsanity.com/paper/1306.5151/) (2013)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [Conditional Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2203.05557/) (2022)
- [Natural Adversarial Examples](https://mlsanity.com/paper/1907.07174/) (2019)
- [Learning Robust Global Representations by Penalizing Local Predictive Power](https://mlsanity.com/paper/1905.13549/) (2019)
- [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://mlsanity.com/paper/2110.07602/) (2021)
- [Efficient Test-Time Model Adaptation without Forgetting](https://mlsanity.com/paper/2204.02610/) (2022)
- [Regularizing Neural Networks by Penalizing Confident Output Distributions](https://mlsanity.com/paper/1701.06548/) (2017)
- [Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models](https://mlsanity.com/paper/2209.07511/) (2022)
- [Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration](https://mlsanity.com/paper/1910.12656/) (2019)
- [Visual-Language Prompt Tuning with Knowledge-guided Context Optimization](https://mlsanity.com/paper/2303.13283/) (2023)
- [What does a platypus look like? Generating customized prompts for zero-shot image classification](https://mlsanity.com/paper/2209.03320/) (2022)
- [Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning](https://mlsanity.com/paper/2308.06038/) (2023)
- [Efficient Test-Time Adaptation of Vision-Language Models](https://mlsanity.com/paper/2403.18293/) (2024)
- [Align Your Prompts: Test-Time Prompting with Distribution Alignment for Zero-Shot Generalization](https://mlsanity.com/paper/2311.01459/) (2023)
- [C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion](https://mlsanity.com/paper/2403.14119/) (2024)
- [A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration](https://mlsanity.com/paper/2203.13834/) (2022)
- [Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology](https://mlsanity.com/paper/2402.14252/) (2024)
- [ArGue: Attribute-Guided Prompt Tuning for Vision-Language Models](https://mlsanity.com/paper/2311.16494/) (2023)

## Built on by (most cited first, 2 of 2)

- [Enhancing Deep Neural Network Reliability with Refinement and Calibration](https://mlsanity.com/paper/2605.23249/) (2026)
- [When Prompts Ignore Structure: Graph-Based Attribute Reasoning for Calibrated VLMs](https://mlsanity.com/paper/2607.07395/) (2026)

## Abstract (arXiv)

Vision-language models (VLM) have demonstrated impressive performance in image recognition by leveraging self-supervised training on large datasets. Their performance can be further improved by adapting to the test sample using test-time prompt tuning (TPT). Unfortunately, the singular focus of TPT approaches on improving the accuracy suffers from tunnel vision, and leads to degradation in confidence calibration. This limits the applicability of TPT in critical applications. We make three contributions in this work. (1) We posit that random or naive initialization of prompts leads to overfitting on a particular test sample, and is the main reason for miscalibration of the VLM after TPT. To mitigate the problem, we propose careful initialization of test time prompt using prior knowledge about the target label attributes from a large language model (LLM); (2) To further maintain the quality of prompts during tpt, we propose a novel regularization loss to reduce intraclass distance, and increase inter-class distance between the learnt Through extensive experiments on different CLIP architectures and 15 datasets, we show that our approach can effectively improve the calibration after TPT. We report an average expected calibration error (ECE) of 4.11 with our method, TCA, compared to 11.7 for vanilla TPT, 6.12 for C-TPT (ICLR'24), 6.78 for DiffTPT (CVPR'23), and 8.43 for PromptAlign (NeurIPS'23). The code is publicly accessible at: https://github.com/rhebbalaguppe/TCAPromptWithoutPanic.