Summary of Learning to Manipulate Artistic Images, by Wei Guo et al.
Learning to Manipulate Artistic Images
by Wei Guo, Yuqi Zhang, De Ma, Qian Zheng
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper proposes an Arbitrary Style Image Manipulation Network (SIM-Net) that leverages semantic-free information as guidance and a region transportation strategy in a self-supervised manner for image generation. Unlike exemplar-based methods, SIM-Net does not require semantic information as input and can generate accurate structures without feature compression. The method balances computational efficiency and high resolution to some extent. Moreover, it facilitates zero-shot style image manipulation. Compared to state-of-the-art methods, SIM-Net demonstrates superiority in both qualitative and quantitative experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper helps artists create new images by changing styles. They propose a new way to do this without needing to know what’s happening in the image. The method is good at creating accurate structures and doesn’t compress important details. It also allows for making changes without needing any extra information. The results are better than current methods, showing that it can create high-quality images. |
Keywords
» Artificial intelligence » Image generation » Self supervised » Zero shot
