Summary of Neural Contrast: Leveraging Generative Editing For Graphic Design Recommendations, by Marian Lupascu et al.
Neural Contrast: Leveraging Generative Editing for Graphic Design Recommendations
by Marian Lupascu, Ionut Mironica, Mihai-Sorin Stupariu
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); Human-Computer Interaction (cs.HC); 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 A novel generative approach to creating visually appealing composites is proposed in this paper, which optimizes both text and background for compatibility. The authors address previous limitations by using a diffusion model that ensures altered regions beneath design assets exhibit low saliency while enhancing contrast. This improves the visibility of design assets. The method uses a combination of techniques to create effective designs, such as semantic segmentation, style transfer, and color correction. The proposed approach is evaluated on various benchmarks, demonstrating its effectiveness in improving the aesthetics of composites. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a new way to make composite images look better by changing the background and text colors. Current methods can be destructive or not very effective. To fix this, researchers developed a machine learning model that adjusts the parts of the image where design elements are placed. This ensures that the design stands out while also making sure the background isn’t altered too much. The new approach is tested on different types of images and shows it can make them look more visually appealing. |
Keywords
» Artificial intelligence » Diffusion model » Machine learning » Semantic segmentation » Style transfer
