Summary of Leveraging Llms For On-the-fly Instruction Guided Image Editing, by Rodrigo Santos et al.
Leveraging LLMs for On-the-Fly Instruction Guided Image Editing
by Rodrigo Santos, João Silva, António Branco
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed method in this paper enables instruction-guided image editing without prior training or fine-tuning. The approach consists of three steps: image captioning and DDIM inversion to obtain edit direction embeddings, followed by actual image editing. This preparation-free method demonstrates effectiveness and competitiveness on the MAGICBRUSH dataset, outperforming recent state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to tell an AI exactly what you want done with a picture, and it makes the changes without needing any setup or practice. That’s what this research paper is all about. The scientists developed a new way to edit images using only natural language instructions, without needing any special preparation beforehand. They tested their method on a challenging dataset and found that it worked well and was even better than some of the best methods already out there. |
Keywords
» Artificial intelligence » Fine tuning » Image captioning
