Summary of Rethink Arbitrary Style Transfer with Transformer and Contrastive Learning, by Zhanjie Zhang et al.
Rethink Arbitrary Style Transfer with Transformer and Contrastive Learning
by Zhanjie Zhang, Jiakai Sun, Guangyuan Li, Lei Zhao, Quanwei Zhang, Zehua Lan, Haolin Yin, Wei Xing, Huaizhong Lin, Zhiwen Zuo
First submitted to arxiv on: 21 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 The proposed technique in this paper aims to improve the quality of arbitrary style transfer by refining alignment between content and style features, understanding relationships among various styles, and capturing style features using a Perception Encoder. Building upon existing methods that use cross-attention or adaptive normalization, the authors introduce Style Consistency Instance Normalization (SCIN) and Instance-based Contrastive Learning (ICL). Experimental results show that the proposed method generates high-quality stylized images with reduced artifacts compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better-looking pictures by changing the style of one image into another. Right now, we can’t do this very well because some styles don’t work together well. The researchers came up with a new way to fix this problem using three main ideas: making sure content and style match, learning how different styles relate to each other, and creating a special tool to capture the essence of style. By doing this, they showed that their method can create high-quality pictures with fewer mistakes than current methods. |
Keywords
» Artificial intelligence » Alignment » Cross attention » Encoder » Style transfer
