Summary of Specify and Edit: Overcoming Ambiguity in Text-based Image Editing, by Ekaterina Iakovleva et al.
Specify and Edit: Overcoming Ambiguity in Text-Based Image Editing
by Ekaterina Iakovleva, Fabio Pizzati, Philip Torr, Stéphane Lathuilière
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 (SANE) pipeline addresses the limitations of text-based editing diffusion models when dealing with ambiguous input instructions. By leveraging a large language model to decompose user requests into specific, well-defined interventions, SANE improves upon existing approaches by incorporating novel denoising guidance strategies. The pipeline’s benefits are demonstrated through experiments on three baselines and two datasets, showcasing improved interpretability, output diversity, and applicability to various edit tasks. This work has implications for the development of more effective and flexible editing systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The proposed SANE pipeline helps computers better understand what people want when they ask for changes to be made to an image. It’s like asking someone to make a specific change to a photo, rather than just saying “make it look nicer.” The system uses a special language model to break down the request into smaller steps, and then applies those steps to the image. This makes the editing process more accurate and flexible. The SANE pipeline is useful for anyone who wants to improve their computer’s ability to understand and respond to natural language requests. |
Keywords
» Artificial intelligence » Diffusion » Language model » Large language model
