Summary of Prompt Diffusion Robustifies Any-modality Prompt Learning, by Yingjun Du et al.
Prompt Diffusion Robustifies Any-Modality Prompt Learning
by Yingjun Du, Gaowen Liu, Yuzhang Shang, Yuguang Yao, Ramana Kompella, Cees G. M. Snoek
First submitted to arxiv on: 26 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel approach to improve the generalizability of prompt-based classifiers by gradually refining prompts using a diffusion model. The conventional method of employing fixed prompts can suffer from distributional shifts that negatively impact performance on unseen samples. To address this issue, the authors propose a prompt diffusion model that optimizes a collection of prompts to obtain over-fitted prompts per sample. This approach enables the training of a generative transition process from a random prompt to its customized prompt. The resulting model is generic, flexible, and modality-agnostic, making it easily embeddable into existing prompt learning methods for textual, visual, or multi-modal prompt learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible for machines to learn new things without needing all the information upfront. They’re good at understanding some words, but not all of them. The problem is that when they see a word they’ve never seen before, they might not know what it means. To fix this, the authors created a way to adjust the words to help the machine learn better. It’s like having a personal tutor for each new word! This method works with different types of data, like pictures or text, and helps machines make better decisions when they don’t have all the information. |
Keywords
» Artificial intelligence » Diffusion model » Multi modal » Prompt
