Summary of Clip Model For Images to Textual Prompts Based on Top-k Neighbors, by Xin Zhang et al.
CLIP Model for Images to Textual Prompts Based on Top-k Neighbors
by Xin Zhang, Xin Zhang, YeMing Cai, Tianzhi Jia
First submitted to arxiv on: 18 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed cost-effective approach for image-to-prompt generation leverages generative models to generate textual prompts without requiring large amounts of annotated data. By dividing the method into two stages – an online stage and an offline stage – the system uses a combination of the CLIP model and K-nearest neighbors (KNN) algorithm to achieve state-of-the-art results. The proposed system consists of two main parts: an offline task and an online task, which collectively demonstrate the highest metric score of 0.612, outperforming other models by significant margins. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a way to turn images into words without needing lots of labeled data. Researchers have developed a new approach that uses artificial intelligence to generate text prompts from images. The method has two parts: one that happens offline and another that happens online. By combining special AI tools called CLIP and K-nearest neighbors, the system can create text prompts that are highly accurate. This is important because it could help us create more realistic computer-generated characters or products. |
Keywords
» Artificial intelligence » Prompt
