Existing vision-language models (VLMs) such as CLIP have showcased an impressive capability to generalize well across various downstream tasks. These models leverage the synergy between visual and textual information, enabling them to understand and reason about the content present in images and text in a unified manner. This article provides a brief overview of CLIP based on few-shot prompt learning, including experimental data and technical characteristics of some methods. The purpose of this review is to provide a reference for researchers who have just started their research in generalizable prompting of CLIP through few-shot training for classification across 15 datasets and also to facilitate the integration of this field by researchers in other downstream tasks.

翻译：现有的视觉语言模型（如CLIP）已展现出在多种下游任务中优异的泛化能力。这些模型利用视觉与文本信息间的协同作用，能够以统一的方式理解并推理图像与文本中的内容。本文基于小样本提示学习对CLIP进行简要综述，涵盖部分方法的实验数据与技术特征。本综述旨在为刚起步的研究者提供参考，帮助他们通过小样本训练在15个数据集上实现CLIP的通用提示分类研究，同时也促进其他下游任务研究者对该领域的融合探索。