Summary of Addressing Image Hallucination in Text-to-image Generation Through Factual Image Retrieval, by Youngsun Lim and Hyunjung Shim
Addressing Image Hallucination in Text-to-Image Generation through Factual Image Retrieval
by Youngsun Lim, Hyunjung Shim
First submitted to arxiv on: 15 Jul 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 methodology addresses the issue of “image hallucination” in text-to-image generation models by leveraging external sources of factual images to generate realistic images that accurately reflect the input text prompts. The authors classify this problem into three types and propose a solution that uses image editing tools such as InstructPix2Pix or IP-Adapter to incorporate factual information from retrieved images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Text-to-image generation has made great progress, but some models can’t always get it right. They sometimes create pictures that don’t make sense or are just plain wrong. This is called “image hallucination”. The authors of this paper took a closer look at this problem and came up with a solution. They found a way to use existing images that are true to reality, and then edit them to match what the text says. This helps create pictures that actually make sense! |
Keywords
» Artificial intelligence » Hallucination » Image generation
