通过少样本提示学习，提升CLIP在15个数据集上的通用分类能力。
Generalizable Prompt Learning of CLIP: A Brief Overview
- 基于少样本提示学习优化CLIP的通用性
- 在15个数据集上验证了跨任务泛化性能
- 适合刚入门CLIP提示学习的研究者参考
现有视觉语言模型（如CLIP）在多种下游任务中展现出优异的泛化能力。这些模型利用视觉与文本信息的协同作用，实现对图像和文本内容的统一理解与推理。本文简要综述了基于少样本提示学习的CLIP方法，包含部分方法的实验数据和技术特征。旨在为初次涉足CLIP通用提示学习的研究者提供参考，尤其针对在15个数据集上的分类任务，并促进该领域与其他下游任务研究的融合。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。