Summary of Adaptive Length Image Tokenization Via Recurrent Allocation, by Shivam Duggal et al.
Adaptive Length Image Tokenization via Recurrent Allocation
by Shivam Duggal, Phillip Isola, Antonio Torralba, William T. Freeman
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
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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 proposes an innovative approach to learn variable-length token representations for 2D images, inspired by human intelligence and large language models. The proposed architecture, which combines encoder-decoder mechanisms with recurrent rollouts, recursively processes 2D image tokens into 1D latent tokens over multiple iterations. This enables compression of images into a variable number of tokens, ranging from 32 to 256, while aligning token count with image entropy, familiarity, and downstream task requirements. The authors validate their tokenizer using reconstruction loss and FID metrics, demonstrating its effectiveness in capturing the essence of images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates an innovative way to describe pictures in a special computer language. It’s like how humans understand more from some words than others. The new method breaks down pictures into smaller parts, like puzzle pieces, and uses those pieces to create a special code that can be used for different tasks. This allows the same picture to be described using different amounts of information, depending on what is important or what task it’s being used for. The authors tested their way and showed that it works well in capturing the essence of pictures. |
Keywords
» Artificial intelligence » Encoder decoder » Token » Tokenizer
