Pre-trained vision-language models (e.g., CLIP) have shown promising zero-shot generalization in many downstream tasks with properly designed text prompts. Instead of relying on hand-engineered prompts, recent works learn prompts using the training data from downstream tasks. While effective, training on domain-specific data reduces a model's generalization capability to unseen new domains. In this work, we propose test-time prompt tuning (TPT), a method that can learn adaptive prompts on the fly with a single test sample. For image classification, TPT optimizes the prompt by minimizing the entropy with confidence selection so that the model has consistent predictions across different augmented views of each test sample. In evaluating generalization to natural distribution shifts, TPT improves the zero-shot top-1 accuracy of CLIP by 3.6% on average, surpassing previous prompt tuning approaches that require additional task-specific training data. In evaluating cross-dataset generalization with unseen categories, TPT performs on par with the state-of-the-art approaches that use additional training data. Project page: https://azshue.github.io/TPT.

本文提出一种叫做测试时提示调整 (TPT) 的方法，可在单个测试样本上实时学习适应性提示，优化提前提供的CLIP模型动态调整提示， 以最小化模型预测时的不确定度，提高CLIP模型的泛化能力。实验结果表明，TPT方法在自然情况下的zero-shot top-1准确率比以往方法的提升3.6％，并达到了使用额外培训数据的最新先进方法的性能水平。

视觉-语言模型中零-shot泛化的测试时提示调整