Summary of Improved Object-based Style Transfer with Single Deep Network, by Harshmohan Kulkarni et al.
Improved Object-Based Style Transfer with Single Deep Network
by Harshmohan Kulkarni, Om Khare, Ninad Barve, Sunil Mane
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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 A novel methodology is proposed for image-to-image style transfer on objects using a single deep convolutional neural network, leveraging the You Only Look Once version 8 (YOLOv8) segmentation model and its backbone neural network. The goal is to enhance visual appeal by transferring artistic styles while preserving object characteristics. This approach combines segmentation and style transfer in a single network, omitting multiple stages or models, for simpler training and deployment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine transforming objects in images into new and exciting styles! Researchers have developed a way to do just that using a special kind of computer program called a neural network. They used a type of neural network called YOLOv8, which is great at finding things in pictures. By combining this with another part of the network, they were able to change the style of objects in images while keeping their original shape and features. This means you could take an ordinary picture and turn it into a work of art! |
Keywords
» Artificial intelligence » Neural network » Style transfer
