Summary of Meta-prompting For Automating Zero-shot Visual Recognition with Llms, by M. Jehanzeb Mirza et al.
Meta-Prompting for Automating Zero-shot Visual Recognition with LLMs
by M. Jehanzeb Mirza, Leonid Karlinsky, Wei Lin, Sivan Doveh, Jakub Micorek, Mateusz Kozinski, Hilde Kuehne, Horst Possegger
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a novel method called Meta-Prompting for Visual Recognition (MPVR) to automate the prompt generation process for zero-shot image recognition tasks. The MPVR approach takes minimal information about the target task, such as its short natural language description and associated class labels, to produce a diverse set of category-specific prompts. These prompts are used to enhance the zero-shot recognition ability of Vision-Language Models (VLMs), achieving significant improvements over existing methods like CLIP. The authors demonstrate the effectiveness of MPVR across various popular image recognition benchmarks, leveraging Large Language Models (LLMs) such as GPT and Mixtral. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a new way to help computers recognize images without any training data. Right now, humans have to create special prompts for this task, but this requires a lot of work. The researchers developed a machine learning model called Meta-Prompting for Visual Recognition (MPVR) that can automatically generate these prompts using just a short description of the task and some information about what the images might show. This approach is more efficient and accurate than previous methods, and it works well on many different types of image recognition tasks. |
Keywords
* Artificial intelligence * Gpt * Machine learning * Prompt * Prompting * Zero shot
