Adapting pre-trained models to open classes is a challenging problem in machine learning. Vision-language models fully explore the knowledge of text modality, demonstrating strong zero-shot recognition performance, which is naturally suited for various open-set problems. More recently, some research focuses on fine-tuning such models to downstream tasks. Prompt tuning methods achieved huge improvements by learning context vectors on few-shot data. However, through the evaluation under open-set adaptation setting with the test data including new classes, we find that there exists a dilemma that learned prompts have worse generalization abilities than hand-crafted prompts. In this paper, we consider combining the advantages of both and come up with a test-time prompt tuning approach, which leverages the maximum concept matching (MCM) scores as dynamic weights to generate an input-conditioned prompt for each image during test. Through extensive experiments on 11 different datasets, we show that our proposed method outperforms all comparison methods on average considering both base and new classes. The code is available at https://github.com/gaozhengqing/TTPT

本研究解决了将预训练模型适应开放类场景的挑战，尤其是在新类出现时提示的通用性不足问题。通过提出一种测试时提示调优的方法，利用最大概念匹配评分生成输入条件的提示，从而增强模型性能。实验表明，该方法在多个数据集上优于现有所有对比方法，具有显著提升效果。

通过测试时提示调优适应开放类的视觉-语言模型