Summary of Spectral Image Tokenizer, by Carlos Esteves et al.
Spectral Image Tokenizer
by Carlos Esteves, Mohammed Suhail, Ameesh Makadia
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 approach is proposed in this paper, which tokenizes images by mapping them to sequences of discrete tokens representing the image spectrum obtained through a discrete wavelet transform (DWT). This coarse-to-fine representation allows for efficient compression, reconstruction of images with varying resolutions without retraining, and improved conditioning for next-token prediction. The tokenizer also enables partial decoding, enabling the reconstruction of a coarse version of the image from generated tokens. Furthermore, it facilitates the use of autoregressive models for image upsampling. The proposed method is evaluated using various metrics, including reconstruction metrics, multiscale image generation, text-guided image upsampling, and editing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine if you could compress an image into a sequence of small pieces that can be easily stored or transmitted. This paper proposes a new way to do just that by breaking down images into small parts called “tokens”. These tokens are arranged in a special order that helps computers generate new images based on existing ones. The new method is better than previous approaches because it allows for more efficient storage and transmission of images, as well as generating higher-quality images with different resolutions. It also enables the creation of partial images that can be used to reconstruct the original image. |
Keywords
» Artificial intelligence » Autoregressive » Image generation » Token » Tokenizer
