Prompt ensembling of Large Language Model (LLM) generated category-specific prompts has emerged as an effective method to enhance zero-shot recognition ability of Vision-Language Models (VLMs). To obtain these category-specific prompts, the present methods rely on hand-crafting the prompts to the LLMs for generating VLM prompts for the downstream tasks. However, this requires manually composing these task-specific prompts and still, they might not cover the diverse set of visual concepts and task-specific styles associated with the categories of interest. To effectively take humans out of the loop and completely automate the prompt generation process for zero-shot recognition, we propose Meta-Prompting for Visual Recognition (MPVR). Taking as input only minimal information about the target task, in the form of its short natural language description, and a list of associated class labels, MPVR automatically produces a diverse set of category-specific prompts resulting in a strong zero-shot classifier. MPVR generalizes effectively across various popular zero-shot image recognition benchmarks belonging to widely different domains when tested with multiple LLMs and VLMs. For example, MPVR obtains a zero-shot recognition improvement over CLIP by up to 19.8% and 18.2% (5.0% and 4.5% on average over 20 datasets) leveraging GPT and Mixtral LLMs, respectively

通过Meta-Prompting for Visual Recognition (MPVR)方法，仅凭目标任务的简短自然语言描述和相关类别标签的最小信息输入，自动产生一组多样的类别特定提示，从而实现强大的零样本分类器。在多个不同领域的流行的零样本图像识别基准上，使用多个LLMs和VLMs测试，MPVR可以有效地推广，比CLIP提高了最高19.8％和18.2％（平均20个数据集上分别为5.0％和4.5％），依赖于GPT和Mixtral LLMs。

利用LLMs自动化零射视觉识别的元提示