Summary of Enat: Rethinking Spatial-temporal Interactions in Token-based Image Synthesis, by Zanlin Ni et al.
ENAT: Rethinking Spatial-temporal Interactions in Token-based Image Synthesis
by Zanlin Ni, Yulin Wang, Renping Zhou, Yizeng Han, Jiayi Guo, Zhiyuan Liu, Yuan Yao, Gao Huang
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper explores the mechanisms behind non-autoregressive Transformers (NATs) in image synthesis. NATs generate decent-quality images in a few steps by progressively revealing latent tokens and padding unrevealed regions with mask tokens. The authors identify two key patterns: spatially, within each step, mask tokens primarily gather information for decoding while visible tokens provide primary information; temporally, interactions concentrate on updating critical token representations. Based on these findings, the authors propose EfficientNAT (ENAT), a NAT model that encourages critical interactions. ENAT improves performance with reduced computational cost and is validated through experiments on ImageNet-256, ImageNet-512, and MS-COCO. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how to make computers create better pictures of objects. It looks at a special type of computer program called non-autoregressive Transformers (NATs). NATs can create good pictures in just a few steps by gradually revealing what the picture should look like. Researchers found that certain parts of the program work together more than others, and they used this information to create a new version of the program called EfficientNAT. EfficientNAT makes better pictures using less computer power and is tested on real-life images. |
Keywords
» Artificial intelligence » Autoregressive » Image synthesis » Mask » Token
